Whisper: Add pip distribution configuration to support pip installations. (#739)

* Whisper: rename whisper to mlx_whisper

* Whisper: add setup.py config for publish

* Whisper: add assets data to setup config

* Whisper: pre-commit for setup.py

* Whisper: Update README.md

* Whisper: Update README.md

* nits

* fix package data

* nit in readme

---------

Co-authored-by: Awni Hannun <awni@apple.com>

whisper/mlx_whisper/__init__.py

@@ -0,0 +1,5 @@
+# Copyright © 2023-2024 Apple Inc.
+
+from . import audio, decoding, load_models
+from .transcribe import transcribe
+from .version import __version__

whisper/mlx_whisper/assets/download_alice.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+
+audio_file=$HOME/.cache/whisper/alice.mp3
+echo $audio_file
+zipf=alice_in_wonderland_librivox_64kb_mp3.zip
+url=https://www.archive.org/download/alice_in_wonderland_librivox/
+curl -LO $url/$zipf
+unzip $zipf
+mv wonderland_ch_02_64kb.mp3 $audio_file
+rm wonderland_* $zipf

whisper/mlx_whisper/audio.py

@@ -0,0 +1,174 @@
+# Copyright © 2023 Apple Inc.
+
+import os
+from functools import lru_cache
+from subprocess import CalledProcessError, run
+from typing import Union
+
+import mlx.core as mx
+import numpy as np
+
+# hard-coded audio hyperparameters
+SAMPLE_RATE = 16000
+N_FFT = 400
+HOP_LENGTH = 160
+CHUNK_LENGTH = 30
+N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000 samples in a 30-second chunk
+N_FRAMES = N_SAMPLES // HOP_LENGTH  # 3000 frames in a mel spectrogram input
+
+N_SAMPLES_PER_TOKEN = HOP_LENGTH * 2  # the initial convolutions has stride 2
+FRAMES_PER_SECOND = SAMPLE_RATE // HOP_LENGTH  # 10ms per audio frame
+TOKENS_PER_SECOND = SAMPLE_RATE // N_SAMPLES_PER_TOKEN  # 20ms per audio token
+
+
+def load_audio(file: str, sr: int = SAMPLE_RATE):
+    """
+    Open an audio file and read as mono waveform, resampling as necessary
+
+    Parameters
+    ----------
+    file: str
+        The audio file to open
+
+    sr: int
+        The sample rate to resample the audio if necessary
+
+    Returns
+    -------
+    A NumPy array containing the audio waveform, in float32 dtype.
+    """
+
+    # This launches a subprocess to decode audio while down-mixing
+    # and resampling as necessary.  Requires the ffmpeg CLI in PATH.
+    # fmt: off
+    cmd = [
+        "ffmpeg",
+        "-nostdin",
+        "-threads", "0",
+        "-i", file,
+        "-f", "s16le",
+        "-ac", "1",
+        "-acodec", "pcm_s16le",
+        "-ar", str(sr),
+        "-"
+    ]
+    # fmt: on
+    try:
+        out = run(cmd, capture_output=True, check=True).stdout
+    except CalledProcessError as e:
+        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+
+    return mx.array(np.frombuffer(out, np.int16)).flatten().astype(mx.float32) / 32768.0
+
+
+def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
+    """
+    Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
+    """
+    if array.shape[axis] > length:
+        sl = [slice(None)] * array.ndim
+        sl[axis] = slice(0, length)
+        array = array[tuple(sl)]
+
+    if array.shape[axis] < length:
+        pad_widths = [(0, 0)] * array.ndim
+        pad_widths[axis] = (0, length - array.shape[axis])
+        array = mx.pad(array, pad_widths)
+
+    return array
+
+
+@lru_cache(maxsize=None)
+def mel_filters(n_mels: int) -> mx.array:
+    """
+    load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
+    Allows decoupling librosa dependency; saved using:
+
+        np.savez_compressed(
+            "mel_filters.npz",
+            mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
+            mel_128=librosa.filters.mel(sr=16000, n_fft=400, n_mels=128),
+        )
+    """
+    assert n_mels in {80, 128}, f"Unsupported n_mels: {n_mels}"
+
+    filename = os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")
+    return mx.load(filename)[f"mel_{n_mels}"]
+
+
+@lru_cache(maxsize=None)
+def hanning(size):
+    return mx.array(np.hanning(size + 1)[:-1])
+
+
+def stft(x, window, nperseg=256, noverlap=None, nfft=None, axis=-1, pad_mode="reflect"):
+    if nfft is None:
+        nfft = nperseg
+    if noverlap is None:
+        noverlap = nfft // 4
+
+    def _pad(x, padding, pad_mode="constant"):
+        if pad_mode == "constant":
+            return mx.pad(x, [(padding, padding)])
+        elif pad_mode == "reflect":
+            prefix = x[1 : padding + 1][::-1]
+            suffix = x[-(padding + 1) : -1][::-1]
+            return mx.concatenate([prefix, x, suffix])
+        else:
+            raise ValueError(f"Invalid pad_mode {pad_mode}")
+
+    padding = nperseg // 2
+    x = _pad(x, padding, pad_mode)
+
+    strides = [noverlap, 1]
+    t = (x.size - nperseg + noverlap) // noverlap
+    shape = [t, nfft]
+    x = mx.as_strided(x, shape=shape, strides=strides)
+    return mx.fft.rfft(x * window)
+
+
+def log_mel_spectrogram(
+    audio: Union[str, np.ndarray],
+    n_mels: int = 80,
+    padding: int = 0,
+):
+    """
+    Compute the log-Mel spectrogram of
+
+    Parameters
+    ----------
+    audio: Union[str, np.ndarray, mx.array], shape = (*)
+        The path to audio or either a NumPy or mlx array containing the audio waveform in 16 kHz
+
+    n_mels: int
+        The number of Mel-frequency filters, only 80 is supported
+
+    padding: int
+        Number of zero samples to pad to the right
+
+    Returns
+    -------
+    mx.array, shape = (80, n_frames)
+        An  array that contains the Mel spectrogram
+    """
+    device = mx.default_device()
+    mx.set_default_device(mx.cpu)
+    if isinstance(audio, str):
+        audio = load_audio(audio)
+    elif not isinstance(audio, mx.array):
+        audio = mx.array(audio)
+
+    if padding > 0:
+        audio = mx.pad(audio, (0, padding))
+    window = hanning(N_FFT)
+    freqs = stft(audio, window, nperseg=N_FFT, noverlap=HOP_LENGTH)
+    magnitudes = freqs[:-1, :].abs().square()
+
+    filters = mel_filters(n_mels)
+    mel_spec = magnitudes @ filters.T
+
+    log_spec = mx.maximum(mel_spec, 1e-10).log10()
+    log_spec = mx.maximum(log_spec, log_spec.max() - 8.0)
+    log_spec = (log_spec + 4.0) / 4.0
+    mx.set_default_device(device)
+    return log_spec

whisper/mlx_whisper/decoding.py

@@ -0,0 +1,725 @@
+# Copyright © 2023 Apple Inc.
+
+import zlib
+from dataclasses import dataclass, field, replace
+from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Union
+
+import mlx.core as mx
+import numpy as np
+from mlx.utils import tree_map
+
+from .audio import CHUNK_LENGTH
+from .tokenizer import Tokenizer, get_tokenizer
+
+
+def compression_ratio(text) -> float:
+    text_bytes = text.encode("utf-8")
+    return len(text_bytes) / len(zlib.compress(text_bytes))
+
+
+def detect_language(
+    model: "Whisper", mel: mx.array, tokenizer: Tokenizer = None
+) -> Tuple[mx.array, List[dict]]:
+    """
+    Detect the spoken language in the audio, and return them as list of strings, along with the ids
+    of the most probable language tokens and the probability distribution over all language tokens.
+    This is performed outside the main decode loop in order to not interfere with kv-caching.
+
+    Returns
+    -------
+    language_tokens : mx.array, shape = (n_audio,)
+        ids of the most probable language tokens, which appears after the startoftranscript token.
+    language_probs : List[Dict[str, float]], length = n_audio
+        list of dictionaries containing the probability distribution over all languages.
+    """
+    if tokenizer is None:
+        tokenizer = get_tokenizer(
+            model.is_multilingual, num_languages=model.num_languages
+        )
+    if (
+        tokenizer.language is None
+        or tokenizer.language_token not in tokenizer.sot_sequence
+    ):
+        raise ValueError(
+            "This model doesn't have language tokens so it can't perform lang id"
+        )
+
+    single = mel.ndim == 2
+    if single:
+        mel = mel[None]
+
+    # skip encoder forward pass if already-encoded audio features were given
+    if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):
+        mel = model.encoder(mel)
+
+    # forward pass using a single token, startoftranscript
+    n_audio = mel.shape[0]
+    x = mx.array([[tokenizer.sot]] * n_audio)  # [n_audio, 1]
+    logits = model.logits(x, mel)[:, 0]
+
+    # collect detected languages; suppress all non-language tokens
+    mask = np.full(logits.shape[-1], -np.inf, dtype=np.float32)
+    mask[list(tokenizer.all_language_tokens)] = 0.0
+    logits += mx.array(mask)
+    language_tokens = mx.argmax(logits, axis=-1)
+    language_token_probs = mx.softmax(logits, axis=-1)
+    language_probs = [
+        {
+            c: language_token_probs[i, j].item()
+            for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes)
+        }
+        for i in range(n_audio)
+    ]
+
+    if single:
+        language_tokens = language_tokens[0]
+        language_probs = language_probs[0]
+
+    return language_tokens, language_probs
+
+
+@dataclass(frozen=True)
+class DecodingOptions:
+    # whether to perform X->X "transcribe" or X->English "translate"
+    task: str = "transcribe"
+
+    # language that the audio is in; uses detected language if None
+    language: Optional[str] = None
+
+    # sampling-related options
+    temperature: float = 0.0
+    sample_len: Optional[int] = None  # maximum number of tokens to sample
+    best_of: Optional[int] = None  # number of independent sample trajectories, if t > 0
+    beam_size: Optional[int] = None  # number of beams in beam search, if t == 0
+    patience: Optional[float] = None  # patience in beam search (arxiv:2204.05424)
+
+    # "alpha" in Google NMT, or None for length norm, when ranking generations
+    # to select which to return among the beams or best-of-N samples
+    length_penalty: Optional[float] = None
+
+    # text or tokens to feed as the prompt or the prefix; for more info:
+    # https://github.com/openai/whisper/discussions/117#discussioncomment-3727051
+    prompt: Optional[Union[str, List[int]]] = None  # for the previous context
+    prefix: Optional[Union[str, List[int]]] = None  # to prefix the current context
+
+    # list of tokens ids (or comma-separated token ids) to suppress
+    # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`
+    suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
+    suppress_blank: bool = True  # this will suppress blank outputs
+
+    # timestamp sampling options
+    without_timestamps: bool = False  # use <|notimestamps|> to sample text tokens only
+    max_initial_timestamp: Optional[float] = 1.0
+
+    # implementation details
+    fp16: bool = True  # use fp16 for most of the calculation
+
+
+@dataclass(frozen=True)
+class DecodingResult:
+    audio_features: mx.array
+    language: str
+    language_probs: Optional[Dict[str, float]] = None
+    tokens: List[int] = field(default_factory=list)
+    text: str = ""
+    avg_logprob: float = np.nan
+    no_speech_prob: float = np.nan
+    temperature: float = np.nan
+    compression_ratio: float = np.nan
+
+
+class Inference:
+    def __init__(self, model: "Whisper", initial_token_length: int):
+        self.model: "Whisper" = model
+        self.initial_token_length = initial_token_length
+        self.kv_cache = None
+
+    def logits(self, tokens: mx.array, audio_features: mx.array) -> mx.array:
+        """Perform a forward pass on the decoder and return per-token logits"""
+        if tokens.shape[-1] > self.initial_token_length:
+            # only need to use the last token except in the first forward pass
+            tokens = tokens[:, -1:]
+
+        logits, self.kv_cache, _ = self.model.decoder(
+            tokens, audio_features, kv_cache=self.kv_cache
+        )
+        return logits.astype(mx.float32)
+
+    def rearrange_kv_cache(self, source_indices):
+        """Update the key-value cache according to the updated beams"""
+        # update the key/value cache to contain the selected sequences
+        if source_indices != list(range(len(source_indices))):
+            self.kv_cache = tree_map(lambda x: x[source_indices], self.kv_cache)
+
+    def reset(self):
+        self.kv_cache = None
+
+
+class SequenceRanker:
+    def rank(
+        self, tokens: List[List[mx.array]], sum_logprobs: List[List[float]]
+    ) -> List[int]:
+        """
+        Given a list of groups of samples and their cumulative log probabilities,
+        return the indices of the samples in each group to select as the final result
+        """
+        raise NotImplementedError
+
+
+class MaximumLikelihoodRanker(SequenceRanker):
+    """
+    Select the sample with the highest log probabilities, penalized using either
+    a simple length normalization or Google NMT paper's length penalty
+    """
+
+    def __init__(self, length_penalty: Optional[float]):
+        self.length_penalty = length_penalty
+
+    def rank(self, tokens: List[List[List[int]]], sum_logprobs: List[List[float]]):
+        def scores(logprobs, lengths):
+            result = []
+            for logprob, length in zip(logprobs, lengths):
+                if self.length_penalty is None:
+                    penalty = length
+                else:
+                    # from the Google NMT paper
+                    penalty = ((5 + length) / 6) ** self.length_penalty
+                result.append(logprob / penalty)
+            return result
+
+        # get the sequence with the highest score
+        lengths = [[len(t) for t in s] for s in tokens]
+        return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)]
+
+
+class TokenDecoder:
+    def reset(self):
+        """Initialize any stateful variables for decoding a new sequence"""
+
+    def update(
+        self, tokens: mx.array, logits: mx.array, sum_logprobs: mx.array
+    ) -> Tuple[mx.array, bool, mx.array]:
+        """Specify how to select the next token, based on the current trace and logits
+
+        Parameters
+        ----------
+        tokens : mx.array, shape = (n_batch, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence tokens
+
+        logits : mx.array, shape = (n_batch, vocab_size)
+            per-token logits of the probability distribution at the current step
+
+        sum_logprobs : mx.array, shape = (n_batch)
+            cumulative log probabilities for each sequence
+
+        Returns
+        -------
+        tokens : mx.array, shape = (n_batch, current_sequence_length + 1)
+            the tokens, appended with the selected next token
+
+        completed : bool
+            True if all sequences has reached the end of text
+
+        sum_logprobs: mx.array, shape = (n_batch)
+            updated cumulative log probabilities for each sequence
+
+        """
+        raise NotImplementedError
+
+    def finalize(
+        self, tokens: mx.array, sum_logprobs: mx.array
+    ) -> Tuple[Sequence[Sequence[mx.array]], List[List[float]]]:
+        """Finalize search and return the final candidate sequences
+
+        Parameters
+        ----------
+        tokens : mx.array, shape = (n_audio, n_group, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence
+
+        sum_logprobs : mx.array, shape = (n_audio, n_group)
+            cumulative log probabilities for each sequence
+
+        Returns
+        -------
+        tokens : Sequence[Sequence[mx.array]], length = n_audio
+            sequence of mx.arrays containing candidate token sequences, for each audio input
+
+        sum_logprobs : List[List[float]], length = n_audio
+            sequence of cumulative log probabilities corresponding to the above
+
+        """
+        raise NotImplementedError
+
+
+class GreedyDecoder(TokenDecoder):
+    def __init__(self, temperature: float, eot: int):
+        self.temperature = temperature
+        self.eot = eot
+
+    def update(
+        self, tokens: mx.array, logits: mx.array, sum_logprobs: mx.array
+    ) -> Tuple[mx.array, bool, mx.array]:
+        if self.temperature == 0:
+            next_tokens = logits.argmax(axis=-1)
+        else:
+            next_tokens = mx.random.categorical(logits=logits / self.temperature)
+
+        next_tokens = mx.argmax(logits, axis=-1)
+        logits = logits.astype(mx.float32)
+        logprobs = logits - mx.logsumexp(logits, axis=-1)
+
+        current_logprobs = logprobs[mx.arange(logprobs.shape[0]), next_tokens]
+        sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot)
+
+        eot_mask = tokens[:, -1] == self.eot
+        next_tokens = next_tokens * (1 - eot_mask) + self.eot * eot_mask
+        tokens = mx.concatenate([tokens, next_tokens[:, None]], axis=-1)
+
+        completed = mx.all(tokens[:, -1] == self.eot)
+        return tokens, completed, sum_logprobs
+
+    def finalize(self, tokens: mx.array, sum_logprobs: mx.array):
+        # make sure each sequence has at least one EOT token at the end
+        tokens = mx.pad(tokens, [(0, 0), (0, 0), (0, 1)], constant_values=self.eot)
+        return tokens, sum_logprobs.tolist()
+
+
+class LogitFilter:
+    def apply(self, logits: mx.array, tokens: mx.array) -> mx.array:
+        """Apply any filtering or masking to logits
+
+        Parameters
+        ----------
+        logits : mx.array, shape = (n_batch, vocab_size)
+            per-token logits of the probability distribution at the current step
+
+        tokens : mx.array, shape = (n_batch, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence tokens
+
+        """
+        raise NotImplementedError
+
+
+class SuppressBlank(LogitFilter):
+    def __init__(self, tokenizer: Tokenizer, sample_begin: int, n_vocab: int):
+        self.sample_begin = sample_begin
+        mask = np.zeros(n_vocab, np.float32)
+        mask[tokenizer.encode(" ") + [tokenizer.eot]] = -np.inf
+        self.mask = mx.array(mask)
+
+    def apply(self, logits: mx.array, tokens: mx.array) -> mx.array:
+        if tokens.shape[1] == self.sample_begin:
+            return logits + self.mask
+        return logits
+
+
+class SuppressTokens(LogitFilter):
+    def __init__(self, suppress_tokens: Sequence[int], n_vocab: int):
+        mask = np.zeros(n_vocab, np.float32)
+        mask[list(suppress_tokens)] = -np.inf
+        self.mask = mx.array(mask)
+
+    def apply(self, logits: mx.array, tokens: mx.array) -> mx.array:
+        return logits + self.mask
+
+
+class ApplyTimestampRules(LogitFilter):
+    def __init__(
+        self,
+        tokenizer: Tokenizer,
+        sample_begin: int,
+        max_initial_timestamp_index: Optional[int],
+    ):
+        self.tokenizer = tokenizer
+        self.sample_begin = sample_begin
+        self.max_initial_timestamp_index = max_initial_timestamp_index
+
+    def apply(self, logits: mx.array, tokens: mx.array) -> mx.array:
+        mask = np.zeros(logits.shape, np.float32)
+        # suppress <|notimestamps|> which is handled by without_timestamps
+        if self.tokenizer.no_timestamps is not None:
+            mask[:, self.tokenizer.no_timestamps] = -np.inf
+
+        # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
+        for k in range(tokens.shape[0]):
+            sampled_tokens = tokens[k, self.sample_begin :]
+            seq = sampled_tokens.tolist()
+            last_was_timestamp = (
+                len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin
+            )
+            penultimate_was_timestamp = (
+                len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin
+            )
+
+            if last_was_timestamp:
+                if penultimate_was_timestamp:  # has to be non-timestamp
+                    mask[k, self.tokenizer.timestamp_begin :] = -np.inf
+                else:  # cannot be normal text tokens
+                    mask[k, : self.tokenizer.eot] = -np.inf
+
+            timestamps = [
+                i for i, v in enumerate(seq) if v > self.tokenizer.timestamp_begin
+            ]
+            if len(timestamps) > 0:
+                # timestamps shouldn't decrease; forbid timestamp tokens smaller than the last
+                # also force each segment to have a nonzero length, to prevent infinite looping
+                last_timestamp = timestamps[-1]
+                if not last_timestamp or penultimate_was_timestamp:
+                    last_timestamp += 1
+                mask[k, self.tokenizer.timestamp_begin : last_timestamp] = -np.inf
+
+        if tokens.shape[1] == self.sample_begin:
+            # suppress generating non-timestamp tokens at the beginning
+            mask[:, : self.tokenizer.timestamp_begin] = -np.inf
+
+            # apply the `max_initial_timestamp` option
+            if self.max_initial_timestamp_index is not None:
+                last_allowed = (
+                    self.tokenizer.timestamp_begin + self.max_initial_timestamp_index
+                )
+                mask[:, last_allowed + 1 :] = -np.inf
+
+        # if sum of probability over timestamps is above any other token, sample timestamp
+        logprobs = logits - mx.logsumexp(logits, axis=-1)
+        for k in range(tokens.shape[0]):
+            timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(
+                axis=-1
+            )
+            max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max()
+            if timestamp_logprob > max_text_token_logprob:
+                mask[k, : self.tokenizer.timestamp_begin] = -np.inf
+
+        return logits + mx.array(mask, logits.dtype)
+
+
+class DecodingTask:
+    inference: Inference
+    sequence_ranker: SequenceRanker
+    decoder: TokenDecoder
+    logit_filters: List[LogitFilter]
+
+    def __init__(self, model: "Whisper", options: DecodingOptions):
+        self.model = model
+
+        language = options.language or "en"
+        tokenizer = get_tokenizer(
+            model.is_multilingual,
+            num_languages=model.num_languages,
+            language=language,
+            task=options.task,
+        )
+        self.tokenizer: Tokenizer = tokenizer
+        self.options: DecodingOptions = self._verify_options(options)
+
+        self.n_group: int = options.beam_size or options.best_of or 1
+        self.n_ctx: int = model.dims.n_text_ctx
+        self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2
+
+        self.sot_sequence: Tuple[int] = tokenizer.sot_sequence
+        if self.options.without_timestamps:
+            self.sot_sequence = tokenizer.sot_sequence_including_notimestamps
+
+        self.initial_tokens: Tuple[int] = self._get_initial_tokens()
+        self.sample_begin: int = len(self.initial_tokens)
+        self.sot_index: int = self.initial_tokens.index(tokenizer.sot)
+
+        # inference: implements the forward pass through the decoder, including kv caching
+        self.inference = Inference(model, len(self.initial_tokens))
+
+        # sequence ranker: implements how to rank a group of sampled sequences
+        self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty)
+
+        # decoder: implements how to select the next tokens, given the autoregressive distribution
+        if options.beam_size is not None:
+            raise NotImplementedError("Beam search decoder is not yet implemented")
+            # self.decoder = BeamSearchDecoder(
+            #    options.beam_size, tokenizer.eot, self.inference, options.patience
+            # )
+        else:
+            self.decoder = GreedyDecoder(options.temperature, tokenizer.eot)
+
+        # logit filters: applies various rules to suppress or penalize certain tokens
+        self.logit_filters = []
+        if self.options.suppress_blank:
+            self.logit_filters.append(
+                SuppressBlank(self.tokenizer, self.sample_begin, model.dims.n_vocab)
+            )
+        if self.options.suppress_tokens:
+            self.logit_filters.append(
+                SuppressTokens(self._get_suppress_tokens(), model.dims.n_vocab)
+            )
+        if not options.without_timestamps:
+            precision = CHUNK_LENGTH / model.dims.n_audio_ctx  # usually 0.02 seconds
+            max_initial_timestamp_index = None
+            if options.max_initial_timestamp:
+                max_initial_timestamp_index = round(
+                    self.options.max_initial_timestamp / precision
+                )
+            self.logit_filters.append(
+                ApplyTimestampRules(
+                    tokenizer, self.sample_begin, max_initial_timestamp_index
+                )
+            )
+
+    def _verify_options(self, options: DecodingOptions) -> DecodingOptions:
+        if options.beam_size is not None and options.best_of is not None:
+            raise ValueError("beam_size and best_of can't be given together")
+        if options.temperature == 0:
+            if options.best_of is not None:
+                raise ValueError("best_of with greedy sampling (T=0) is not compatible")
+        if options.patience is not None and options.beam_size is None:
+            raise ValueError("patience requires beam_size to be given")
+        if options.length_penalty is not None and not (
+            0 <= options.length_penalty <= 1
+        ):
+            raise ValueError("length_penalty (alpha) should be a value between 0 and 1")
+
+        return options
+
+    def _get_initial_tokens(self) -> Tuple[int]:
+        tokens = list(self.sot_sequence)
+
+        if prefix := self.options.prefix:
+            prefix_tokens = (
+                self.tokenizer.encode(" " + prefix.strip())
+                if isinstance(prefix, str)
+                else prefix
+            )
+            if self.sample_len is not None:
+                max_prefix_len = self.n_ctx // 2 - self.sample_len
+                prefix_tokens = prefix_tokens[-max_prefix_len:]
+            tokens = tokens + prefix_tokens
+
+        if prompt := self.options.prompt:
+            prompt_tokens = (
+                self.tokenizer.encode(" " + prompt.strip())
+                if isinstance(prompt, str)
+                else prompt
+            )
+            tokens = (
+                [self.tokenizer.sot_prev]
+                + prompt_tokens[-(self.n_ctx // 2 - 1) :]
+                + tokens
+            )
+
+        return tuple(tokens)
+
+    def _get_suppress_tokens(self) -> Tuple[int]:
+        suppress_tokens = self.options.suppress_tokens
+
+        if isinstance(suppress_tokens, str):
+            suppress_tokens = [int(t) for t in suppress_tokens.split(",")]
+
+        if -1 in suppress_tokens:
+            suppress_tokens = [t for t in suppress_tokens if t >= 0]
+            suppress_tokens.extend(self.tokenizer.non_speech_tokens)
+        elif suppress_tokens is None or len(suppress_tokens) == 0:
+            suppress_tokens = []  # interpret empty string as an empty list
+        else:
+            assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
+
+        suppress_tokens.extend(
+            [
+                self.tokenizer.transcribe,
+                self.tokenizer.translate,
+                self.tokenizer.sot,
+                self.tokenizer.sot_prev,
+                self.tokenizer.sot_lm,
+            ]
+        )
+        if self.tokenizer.no_speech is not None:
+            # no-speech probability is collected separately
+            suppress_tokens.append(self.tokenizer.no_speech)
+
+        return tuple(sorted(set(suppress_tokens)))
+
+    def _get_audio_features(self, mel: mx.array):
+        if self.options.fp16:
+            mel = mel.astype(mx.float16)
+
+        if mel.shape[-2:] == (
+            self.model.dims.n_audio_ctx,
+            self.model.dims.n_audio_state,
+        ):
+            # encoded audio features are given; skip audio encoding
+            audio_features = mel
+        else:
+            audio_features = self.model.encoder(mel)
+
+        if audio_features.dtype != (mx.float16 if self.options.fp16 else mx.float32):
+            raise TypeError(
+                f"audio_features has an incorrect dtype: {audio_features.dtype}"
+            )
+
+        return audio_features
+
+    def _detect_language(self, audio_features: mx.array, tokens: np.array):
+        languages = [self.options.language] * audio_features.shape[0]
+        lang_probs = None
+
+        if self.options.language is None or self.options.task == "lang_id":
+            lang_tokens, lang_probs = self.model.detect_language(
+                audio_features, self.tokenizer
+            )
+            languages = [max(probs, key=probs.get) for probs in lang_probs]
+            if self.options.language is None:
+                # write language tokens
+                tokens[:, self.sot_index + 1] = np.array(lang_tokens)
+
+        return languages, lang_probs
+
+    def _main_loop(self, audio_features: mx.array, tokens: mx.array):
+        n_batch = tokens.shape[0]
+        sum_logprobs: mx.array = mx.zeros(n_batch)
+        no_speech_probs = [np.nan] * n_batch
+
+        try:
+            for i in range(self.sample_len):
+                logits = self.inference.logits(tokens, audio_features)
+
+                if (
+                    i == 0 and self.tokenizer.no_speech is not None
+                ):  # save no_speech_probs
+                    probs_at_sot = mx.softmax(
+                        logits[:, self.sot_index].astype(mx.float32), axis=-1
+                    )
+                    no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
+
+                # now we need to consider the logits at the last token only
+                logits = logits[:, -1]
+
+                # apply the logit filters, e.g. for suppressing or applying penalty to
+                for logit_filter in self.logit_filters:
+                    logits = logit_filter.apply(logits, tokens)
+
+                # expand the tokens tensor with the selected next tokens
+                tokens, completed, sum_logprobs = self.decoder.update(
+                    tokens, logits, sum_logprobs
+                )
+
+                if completed or tokens.shape[-1] > self.n_ctx:
+                    break
+        finally:
+            self.inference.reset()
+
+        return tokens, sum_logprobs, no_speech_probs
+
+    def run(self, mel: mx.array) -> List[DecodingResult]:
+        self.decoder.reset()
+        tokenizer: Tokenizer = self.tokenizer
+        n_audio: int = mel.shape[0]
+
+        audio_features: mx.array = self._get_audio_features(mel)  # encoder forward pass
+        tokens: np.array = np.array(self.initial_tokens)
+        tokens = np.broadcast_to(tokens, (n_audio, len(self.initial_tokens))).copy()
+
+        # detect language if requested, overwriting the language token
+        languages, language_probs = self._detect_language(audio_features, tokens)
+        if self.options.task == "lang_id":
+            return [
+                DecodingResult(
+                    audio_features=features, language=language, language_probs=probs
+                )
+                for features, language, probs in zip(
+                    audio_features, languages, language_probs
+                )
+            ]
+
+        # repeat tokens by the group size, for beam search or best-of-n sampling
+        tokens = mx.array(tokens)
+        if self.n_group > 1:
+            tokens = tokens[:, None, :]
+            tokens = mx.broadcast_to(
+                tokens, [n_audio, self.n_group, len(self.initial_tokens)]
+            )
+            tokens = tokens.reshape(
+                tokens, (n_audio * self.n_group, len(self.initial_tokens))
+            )
+
+        # call the main sampling loop
+        tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens)
+
+        # reshape the tensors to have (n_audio, n_group) as the first two dimensions
+        audio_features = audio_features[:: self.n_group]
+        no_speech_probs = no_speech_probs[:: self.n_group]
+        assert audio_features.shape[0] == len(no_speech_probs) == n_audio
+
+        tokens = tokens.reshape(n_audio, self.n_group, -1)
+        sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
+
+        # get the final candidates for each group, and slice between the first sampled token and EOT
+        tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
+        tokens = tokens[..., self.sample_begin :].tolist()
+        tokens = [[t[: t.index(tokenizer.eot)] for t in s] for s in tokens]
+
+        # select the top-ranked sample in each group
+        selected = self.sequence_ranker.rank(tokens, sum_logprobs)
+        tokens: List[List[int]] = [t[i] for i, t in zip(selected, tokens)]
+        texts: List[str] = [tokenizer.decode(t).strip() for t in tokens]
+
+        sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)]
+        avg_logprobs: List[float] = [
+            lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)
+        ]
+
+        fields = (
+            texts,
+            languages,
+            tokens,
+            audio_features,
+            avg_logprobs,
+            no_speech_probs,
+        )
+        if len(set(map(len, fields))) != 1:
+            raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}")
+
+        return [
+            DecodingResult(
+                audio_features=features,
+                language=language,
+                tokens=tokens,
+                text=text,
+                avg_logprob=avg_logprob,
+                no_speech_prob=no_speech_prob,
+                temperature=self.options.temperature,
+                compression_ratio=compression_ratio(text),
+            )
+            for text, language, tokens, features, avg_logprob, no_speech_prob in zip(
+                *fields
+            )
+        ]
+
+
+def decode(
+    model: "Whisper",
+    mel: mx.array,
+    options: DecodingOptions = DecodingOptions(),
+    **kwargs,
+) -> Union[DecodingResult, List[DecodingResult]]:
+    """
+    Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
+
+    Parameters
+    ----------
+    model: Whisper
+        the Whisper model instance
+
+    mel: mx.array, shape = (80, 3000) or (*, 80, 3000)
+        An array containing the Mel spectrogram(s)
+
+    options: DecodingOptions
+        A dataclass that contains all necessary options for decoding 30-second segments
+
+    Returns
+    -------
+    result: Union[DecodingResult, List[DecodingResult]]
+        The result(s) of decoding contained in `DecodingResult` dataclass instance(s)
+    """
+    if single := mel.ndim == 2:
+        mel = mel[None]
+
+    if kwargs:
+        options = replace(options, **kwargs)
+
+    result = DecodingTask(model, options).run(mel)
+    return result[0] if single else result

whisper/mlx_whisper/load_models.py

@@ -0,0 +1,43 @@
+# Copyright © 2023 Apple Inc.
+
+import json
+from pathlib import Path
+
+import mlx.core as mx
+import mlx.nn as nn
+from huggingface_hub import snapshot_download
+from mlx.utils import tree_unflatten
+
+from . import whisper
+
+
+def load_model(
+    path_or_hf_repo: str,
+    dtype: mx.Dtype = mx.float32,
+) -> whisper.Whisper:
+    model_path = Path(path_or_hf_repo)
+    if not model_path.exists():
+        model_path = Path(snapshot_download(repo_id=path_or_hf_repo))
+
+    with open(str(model_path / "config.json"), "r") as f:
+        config = json.loads(f.read())
+        config.pop("model_type", None)
+        quantization = config.pop("quantization", None)
+
+    model_args = whisper.ModelDimensions(**config)
+
+    weights = mx.load(str(model_path / "weights.npz"))
+
+    model = whisper.Whisper(model_args, dtype)
+
+    if quantization is not None:
+        class_predicate = (
+            lambda p, m: isinstance(m, (nn.Linear, nn.Embedding))
+            and f"{p}.scales" in weights
+        )
+        nn.quantize(model, **quantization, class_predicate=class_predicate)
+
+    weights = tree_unflatten(list(weights.items()))
+    model.update(weights)
+    mx.eval(model.parameters())
+    return model

whisper/mlx_whisper/requirements.txt

@@ -0,0 +1,9 @@
+mlx>=0.11
+numba
+numpy
+torch
+tqdm
+more-itertools
+tiktoken
+huggingface_hub
+scipy

whisper/mlx_whisper/timing.py

@@ -0,0 +1,330 @@
+# Copyright © 2023 Apple Inc.
+
+import itertools
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List
+
+import mlx.core as mx
+import numba
+import numpy as np
+from scipy import signal
+
+from .audio import HOP_LENGTH, SAMPLE_RATE, TOKENS_PER_SECOND
+from .tokenizer import Tokenizer
+
+if TYPE_CHECKING:
+    from .model import Whisper
+
+
+def median_filter(x: np.ndarray, filter_width: int):
+    """Apply a median filter of width `filter_width` along the last dimension of `x`"""
+    pad_width = filter_width // 2
+    if x.shape[-1] <= pad_width:
+        # F.pad requires the padding width to be smaller than the input dimension
+        return x
+
+    if (ndim := x.ndim) <= 2:
+        # `F.pad` does not support 1D or 2D inputs for reflect padding but supports 3D and 4D
+        x = x[None, None, :]
+
+    assert (
+        filter_width > 0 and filter_width % 2 == 1
+    ), "`filter_width` should be an odd number"
+
+    x = np.pad(x, ((0, 0), (0, 0), (pad_width, pad_width)), mode="reflect")
+
+    # todo: more efficient version in mlx
+    result = signal.medfilt(x.astype(np.float32), kernel_size=(1, 1, filter_width))[
+        ..., pad_width:-pad_width
+    ]
+
+    if ndim <= 2:
+        result = result[0, 0]
+
+    return result
+
+
+@numba.jit(nopython=True)
+def backtrace(trace: np.ndarray):
+    i = trace.shape[0] - 1
+    j = trace.shape[1] - 1
+    trace[0, :] = 2
+    trace[:, 0] = 1
+
+    result = []
+    while i > 0 or j > 0:
+        result.append((i - 1, j - 1))
+
+        if trace[i, j] == 0:
+            i -= 1
+            j -= 1
+        elif trace[i, j] == 1:
+            i -= 1
+        elif trace[i, j] == 2:
+            j -= 1
+        else:
+            raise ValueError("Unexpected trace[i, j]")
+
+    result = np.array(result)
+    return result[::-1, :].T
+
+
+@numba.jit(nopython=True, parallel=True)
+def dtw_cpu(x: np.ndarray):
+    N, M = x.shape
+    cost = np.ones((N + 1, M + 1), dtype=np.float32) * np.inf
+    trace = -np.ones((N + 1, M + 1), dtype=np.float32)
+
+    cost[0, 0] = 0
+    for j in range(1, M + 1):
+        for i in range(1, N + 1):
+            c0 = cost[i - 1, j - 1]
+            c1 = cost[i - 1, j]
+            c2 = cost[i, j - 1]
+
+            if c0 < c1 and c0 < c2:
+                c, t = c0, 0
+            elif c1 < c0 and c1 < c2:
+                c, t = c1, 1
+            else:
+                c, t = c2, 2
+
+            cost[i, j] = x[i - 1, j - 1] + c
+            trace[i, j] = t
+
+    return backtrace(trace)
+
+
+def dtw(x: np.ndarray) -> np.ndarray:
+    # todo: more efficient version in mlx
+    return dtw_cpu(x)
+
+
+@dataclass
+class WordTiming:
+    word: str
+    tokens: List[int]
+    start: float
+    end: float
+    probability: float
+
+
+def find_alignment(
+    model: "Whisper",
+    tokenizer: Tokenizer,
+    text_tokens: List[int],
+    mel: mx.array,
+    num_frames: int,
+    *,
+    medfilt_width: int = 7,
+    qk_scale: float = 1.0,
+) -> List[WordTiming]:
+    if len(text_tokens) == 0:
+        return []
+
+    tokens = mx.array(
+        [
+            *tokenizer.sot_sequence,
+            tokenizer.no_timestamps,
+            *text_tokens,
+            tokenizer.eot,
+        ]
+    )
+
+    logits, cross_qk = model.forward_with_cross_qk(mel[None, :], tokens[None, :])
+    # consider only the logits associated with predicting text
+    sampled_logits = logits[0][len(tokenizer.sot_sequence) : -2, : tokenizer.eot]
+    token_probs = mx.softmax(sampled_logits.astype(mx.float32), axis=-1).astype(
+        sampled_logits.dtype
+    )
+    text_token_probs = mx.take_along_axis(
+        token_probs, mx.array(text_tokens)[:, None], axis=1
+    ).squeeze(1)
+    text_token_probs = np.array(text_token_probs)
+
+    # heads * tokens * frames
+    weights = mx.stack(
+        [cross_qk[_l.item()][0, _h.item()] for _l, _h in model.alignment_heads]
+    )
+    weights = weights[:, :, : num_frames // 2]
+    weights = mx.softmax(weights * qk_scale, axis=-1)
+    mean = mx.mean(weights, axis=-2, keepdims=True)
+    std = mx.var(weights, axis=-2, keepdims=True, ddof=0).sqrt()
+    weights = (weights - mean) / std
+    weights = median_filter(np.array(weights), medfilt_width)
+
+    matrix = weights.mean(axis=0)
+    matrix = matrix[len(tokenizer.sot_sequence) : -1]
+    text_indices, time_indices = dtw(-matrix)
+
+    words, word_tokens = tokenizer.split_to_word_tokens(text_tokens + [tokenizer.eot])
+    if len(word_tokens) <= 1:
+        # return on eot only
+        # >>> np.pad([], (1, 0))
+        # array([0.])
+        # This results in crashes when we lookup jump_times with float, like
+        # IndexError: arrays used as indices must be of integer (or boolean) type
+        return []
+    word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0))
+
+    jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool)
+    jump_times = time_indices[jumps] / TOKENS_PER_SECOND
+    start_times = jump_times[word_boundaries[:-1]]
+    end_times = jump_times[word_boundaries[1:]]
+    word_probabilities = [
+        np.mean(text_token_probs[i:j])
+        for i, j in zip(word_boundaries[:-1], word_boundaries[1:])
+    ]
+
+    return [
+        WordTiming(word, tokens, start, end, probability)
+        for word, tokens, start, end, probability in zip(
+            words, word_tokens, start_times, end_times, word_probabilities
+        )
+    ]
+
+
+def merge_punctuations(alignment: List[WordTiming], prepended: str, appended: str):
+    # merge prepended punctuations
+    i = len(alignment) - 2
+    j = len(alignment) - 1
+    while i >= 0:
+        previous = alignment[i]
+        following = alignment[j]
+        if previous.word.startswith(" ") and previous.word.strip() in prepended:
+            # prepend it to the following word
+            following.word = previous.word + following.word
+            following.tokens = previous.tokens + following.tokens
+            previous.word = ""
+            previous.tokens = []
+        else:
+            j = i
+        i -= 1
+
+    # merge appended punctuations
+    i = 0
+    j = 1
+    while j < len(alignment):
+        previous = alignment[i]
+        following = alignment[j]
+        if not previous.word.endswith(" ") and following.word in appended:
+            # append it to the previous word
+            previous.word = previous.word + following.word
+            previous.tokens = previous.tokens + following.tokens
+            following.word = ""
+            following.tokens = []
+        else:
+            i = j
+        j += 1
+
+
+def add_word_timestamps(
+    *,
+    segments: List[dict],
+    model: "Whisper",
+    tokenizer: Tokenizer,
+    mel: mx.array,
+    num_frames: int,
+    prepend_punctuations: str = "\"'“¿([{-",
+    append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
+    last_speech_timestamp: float,
+    **kwargs,
+):
+    if len(segments) == 0:
+        return
+
+    text_tokens_per_segment = [
+        [token for token in segment["tokens"] if token < tokenizer.eot]
+        for segment in segments
+    ]
+
+    text_tokens = list(itertools.chain.from_iterable(text_tokens_per_segment))
+    alignment = find_alignment(model, tokenizer, text_tokens, mel, num_frames, **kwargs)
+    word_durations = np.array([t.end - t.start for t in alignment])
+    word_durations = word_durations[word_durations.nonzero()]
+    median_duration = np.median(word_durations) if len(word_durations) > 0 else 0.0
+    median_duration = min(0.7, float(median_duration))
+    max_duration = median_duration * 2
+
+    # hack: truncate long words at sentence boundaries.
+    # a better segmentation algorithm based on VAD should be able to replace this.
+    if len(word_durations) > 0:
+        sentence_end_marks = ".。!！?？"
+        # ensure words at sentence boundaries are not longer than twice the median word duration.
+        for i in range(1, len(alignment)):
+            if alignment[i].end - alignment[i].start > max_duration:
+                if alignment[i].word in sentence_end_marks:
+                    alignment[i].end = alignment[i].start + max_duration
+                elif alignment[i - 1].word in sentence_end_marks:
+                    alignment[i].start = alignment[i].end - max_duration
+
+    merge_punctuations(alignment, prepend_punctuations, append_punctuations)
+
+    time_offset = segments[0]["seek"] * HOP_LENGTH / SAMPLE_RATE
+    word_index = 0
+
+    for segment, text_tokens in zip(segments, text_tokens_per_segment):
+        saved_tokens = 0
+        words = []
+
+        while word_index < len(alignment) and saved_tokens < len(text_tokens):
+            timing = alignment[word_index]
+
+            if timing.word:
+                words.append(
+                    dict(
+                        word=timing.word,
+                        start=round(time_offset + timing.start, 2),
+                        end=round(time_offset + timing.end, 2),
+                        probability=timing.probability,
+                    )
+                )
+
+            saved_tokens += len(timing.tokens)
+            word_index += 1
+
+        # hack: truncate long words at segment boundaries.
+        # a better segmentation algorithm based on VAD should be able to replace this.
+        if len(words) > 0:
+            # ensure the first and second word after a pause is not longer than
+            # twice the median word duration.
+            if words[0]["end"] - last_speech_timestamp > median_duration * 4 and (
+                words[0]["end"] - words[0]["start"] > max_duration
+                or (
+                    len(words) > 1
+                    and words[1]["end"] - words[0]["start"] > max_duration * 2
+                )
+            ):
+                if (
+                    len(words) > 1
+                    and words[1]["end"] - words[1]["start"] > max_duration
+                ):
+                    boundary = max(words[1]["end"] / 2, words[1]["end"] - max_duration)
+                    words[0]["end"] = words[1]["start"] = boundary
+                words[0]["start"] = max(0, words[0]["end"] - max_duration)
+
+            # prefer the segment-level start timestamp if the first word is too long.
+            if (
+                segment["start"] < words[0]["end"]
+                and segment["start"] - 0.5 > words[0]["start"]
+            ):
+                words[0]["start"] = max(
+                    0, min(words[0]["end"] - median_duration, segment["start"])
+                )
+            else:
+                segment["start"] = words[0]["start"]
+
+            # prefer the segment-level end timestamp if the last word is too long.
+            if (
+                segment["end"] > words[-1]["start"]
+                and segment["end"] + 0.5 < words[-1]["end"]
+            ):
+                words[-1]["end"] = max(
+                    words[-1]["start"] + median_duration, segment["end"]
+                )
+            else:
+                segment["end"] = words[-1]["end"]
+
+            last_speech_timestamp = segment["end"]
+
+        segment["words"] = words

whisper/mlx_whisper/tokenizer.py

@@ -0,0 +1,398 @@
+# Copyright © 2023 Apple Inc.
+
+import base64
+import os
+import string
+from dataclasses import dataclass, field
+from functools import cached_property, lru_cache
+from typing import Dict, List, Optional, Tuple
+
+import tiktoken
+
+LANGUAGES = {
+    "en": "english",
+    "zh": "chinese",
+    "de": "german",
+    "es": "spanish",
+    "ru": "russian",
+    "ko": "korean",
+    "fr": "french",
+    "ja": "japanese",
+    "pt": "portuguese",
+    "tr": "turkish",
+    "pl": "polish",
+    "ca": "catalan",
+    "nl": "dutch",
+    "ar": "arabic",
+    "sv": "swedish",
+    "it": "italian",
+    "id": "indonesian",
+    "hi": "hindi",
+    "fi": "finnish",
+    "vi": "vietnamese",
+    "he": "hebrew",
+    "uk": "ukrainian",
+    "el": "greek",
+    "ms": "malay",
+    "cs": "czech",
+    "ro": "romanian",
+    "da": "danish",
+    "hu": "hungarian",
+    "ta": "tamil",
+    "no": "norwegian",
+    "th": "thai",
+    "ur": "urdu",
+    "hr": "croatian",
+    "bg": "bulgarian",
+    "lt": "lithuanian",
+    "la": "latin",
+    "mi": "maori",
+    "ml": "malayalam",
+    "cy": "welsh",
+    "sk": "slovak",
+    "te": "telugu",
+    "fa": "persian",
+    "lv": "latvian",
+    "bn": "bengali",
+    "sr": "serbian",
+    "az": "azerbaijani",
+    "sl": "slovenian",
+    "kn": "kannada",
+    "et": "estonian",
+    "mk": "macedonian",
+    "br": "breton",
+    "eu": "basque",
+    "is": "icelandic",
+    "hy": "armenian",
+    "ne": "nepali",
+    "mn": "mongolian",
+    "bs": "bosnian",
+    "kk": "kazakh",
+    "sq": "albanian",
+    "sw": "swahili",
+    "gl": "galician",
+    "mr": "marathi",
+    "pa": "punjabi",
+    "si": "sinhala",
+    "km": "khmer",
+    "sn": "shona",
+    "yo": "yoruba",
+    "so": "somali",
+    "af": "afrikaans",
+    "oc": "occitan",
+    "ka": "georgian",
+    "be": "belarusian",
+    "tg": "tajik",
+    "sd": "sindhi",
+    "gu": "gujarati",
+    "am": "amharic",
+    "yi": "yiddish",
+    "lo": "lao",
+    "uz": "uzbek",
+    "fo": "faroese",
+    "ht": "haitian creole",
+    "ps": "pashto",
+    "tk": "turkmen",
+    "nn": "nynorsk",
+    "mt": "maltese",
+    "sa": "sanskrit",
+    "lb": "luxembourgish",
+    "my": "myanmar",
+    "bo": "tibetan",
+    "tl": "tagalog",
+    "mg": "malagasy",
+    "as": "assamese",
+    "tt": "tatar",
+    "haw": "hawaiian",
+    "ln": "lingala",
+    "ha": "hausa",
+    "ba": "bashkir",
+    "jw": "javanese",
+    "su": "sundanese",
+    "yue": "cantonese",
+}
+
+# language code lookup by name, with a few language aliases
+TO_LANGUAGE_CODE = {
+    **{language: code for code, language in LANGUAGES.items()},
+    "burmese": "my",
+    "valencian": "ca",
+    "flemish": "nl",
+    "haitian": "ht",
+    "letzeburgesch": "lb",
+    "pushto": "ps",
+    "panjabi": "pa",
+    "moldavian": "ro",
+    "moldovan": "ro",
+    "sinhalese": "si",
+    "castilian": "es",
+    "mandarin": "zh",
+}
+
+
+@dataclass
+class Tokenizer:
+    """A thin wrapper around `tiktoken` providing quick access to special tokens"""
+
+    encoding: tiktoken.Encoding
+    num_languages: int
+    language: Optional[str] = None
+    task: Optional[str] = None
+    sot_sequence: Tuple[int] = ()
+    special_tokens: Dict[str, int] = field(default_factory=dict)
+
+    def __post_init__(self):
+        for special in self.encoding.special_tokens_set:
+            special_token = self.encoding.encode_single_token(special)
+            self.special_tokens[special] = special_token
+
+        sot: int = self.special_tokens["<|startoftranscript|>"]
+        translate: int = self.special_tokens["<|translate|>"]
+        transcribe: int = self.special_tokens["<|transcribe|>"]
+
+        langs = tuple(LANGUAGES.keys())[: self.num_languages]
+        sot_sequence = [sot]
+        if self.language is not None:
+            sot_sequence.append(sot + 1 + langs.index(self.language))
+        if self.task is not None:
+            task_token: int = transcribe if self.task == "transcribe" else translate
+            sot_sequence.append(task_token)
+
+        self.sot_sequence = tuple(sot_sequence)
+
+    def encode(self, text, **kwargs):
+        return self.encoding.encode(text, **kwargs)
+
+    def decode(self, token_ids: List[int], **kwargs) -> str:
+        token_ids = [t for t in token_ids if t < self.timestamp_begin]
+        return self.encoding.decode(token_ids, **kwargs)
+
+    def decode_with_timestamps(self, token_ids: List[int], **kwargs) -> str:
+        """
+        Timestamp tokens are above other special tokens' id range and are ignored by `decode()`.
+        This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
+        """
+        return self.encoding.decode(token_ids, **kwargs)
+
+    @cached_property
+    def eot(self) -> int:
+        return self.encoding.eot_token
+
+    @cached_property
+    def transcribe(self) -> int:
+        return self.special_tokens["<|transcribe|>"]
+
+    @cached_property
+    def translate(self) -> int:
+        return self.special_tokens["<|translate|>"]
+
+    @cached_property
+    def sot(self) -> int:
+        return self.special_tokens["<|startoftranscript|>"]
+
+    @cached_property
+    def sot_lm(self) -> int:
+        return self.special_tokens["<|startoflm|>"]
+
+    @cached_property
+    def sot_prev(self) -> int:
+        return self.special_tokens["<|startofprev|>"]
+
+    @cached_property
+    def no_speech(self) -> int:
+        return self.special_tokens["<|nospeech|>"]
+
+    @cached_property
+    def no_timestamps(self) -> int:
+        return self.special_tokens["<|notimestamps|>"]
+
+    @cached_property
+    def timestamp_begin(self) -> int:
+        return self.special_tokens["<|0.00|>"]
+
+    @cached_property
+    def language_token(self) -> int:
+        """Returns the token id corresponding to the value of the `language` field"""
+        if self.language is None:
+            raise ValueError("This tokenizer does not have language token configured")
+
+        return self.to_language_token(self.language)
+
+    def to_language_token(self, language):
+        if token := self.special_tokens.get(f"<|{language}|>", None):
+            return token
+
+        raise KeyError(f"Language {language} not found in tokenizer.")
+
+    @cached_property
+    def all_language_tokens(self) -> Tuple[int]:
+        result = []
+        for token, token_id in self.special_tokens.items():
+            if token.strip("<|>") in LANGUAGES:
+                result.append(token_id)
+        return tuple(result)[: self.num_languages]
+
+    @cached_property
+    def all_language_codes(self) -> Tuple[str]:
+        return tuple(self.decode([_l]).strip("<|>") for _l in self.all_language_tokens)
+
+    @cached_property
+    def sot_sequence_including_notimestamps(self) -> Tuple[int]:
+        return tuple(list(self.sot_sequence) + [self.no_timestamps])
+
+    @cached_property
+    def non_speech_tokens(self) -> Tuple[int]:
+        """
+        Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
+        annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
+
+        - ♪♪♪
+        - ( SPEAKING FOREIGN LANGUAGE )
+        - [DAVID] Hey there,
+
+        keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
+        """
+        symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』')
+        symbols += (
+            "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
+        )
+
+        # symbols that may be a single token or multiple tokens depending on the tokenizer.
+        # In case they're multiple tokens, suppress the first token, which is safe because:
+        # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
+        # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
+        miscellaneous = set("♩♪♫♬♭♮♯")
+        assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
+
+        # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
+        result = {self.encoding.encode(" -")[0], self.encoding.encode(" '")[0]}
+        for symbol in symbols + list(miscellaneous):
+            for tokens in [
+                self.encoding.encode(symbol),
+                self.encoding.encode(" " + symbol),
+            ]:
+                if len(tokens) == 1 or symbol in miscellaneous:
+                    result.add(tokens[0])
+
+        return tuple(sorted(result))
+
+    def split_to_word_tokens(self, tokens: List[int]):
+        if self.language in {"zh", "ja", "th", "lo", "my", "yue"}:
+            # These languages don't typically use spaces, so it is difficult to split words
+            # without morpheme analysis. Here, we instead split words at any
+            # position where the tokens are decoded as valid unicode points
+            return self.split_tokens_on_unicode(tokens)
+
+        return self.split_tokens_on_spaces(tokens)
+
+    def split_tokens_on_unicode(self, tokens: List[int]):
+        decoded_full = self.decode_with_timestamps(tokens)
+        replacement_char = "\ufffd"
+
+        words = []
+        word_tokens = []
+        current_tokens = []
+        unicode_offset = 0
+
+        for token in tokens:
+            current_tokens.append(token)
+            decoded = self.decode_with_timestamps(current_tokens)
+
+            if (
+                replacement_char not in decoded
+                or decoded_full[unicode_offset + decoded.index(replacement_char)]
+                == replacement_char
+            ):
+                words.append(decoded)
+                word_tokens.append(current_tokens)
+                current_tokens = []
+                unicode_offset += len(decoded)
+
+        return words, word_tokens
+
+    def split_tokens_on_spaces(self, tokens: List[int]):
+        subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens)
+        words = []
+        word_tokens = []
+
+        for subword, subword_tokens in zip(subwords, subword_tokens_list):
+            special = subword_tokens[0] >= self.eot
+            with_space = subword.startswith(" ")
+            punctuation = subword.strip() in string.punctuation
+            if special or with_space or punctuation or len(words) == 0:
+                words.append(subword)
+                word_tokens.append(subword_tokens)
+            else:
+                words[-1] = words[-1] + subword
+                word_tokens[-1].extend(subword_tokens)
+
+        return words, word_tokens
+
+
+@lru_cache(maxsize=None)
+def get_encoding(name: str = "gpt2", num_languages: int = 99):
+    vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken")
+    with open(vocab_path) as fid:
+        ranks = {
+            base64.b64decode(token): int(rank)
+            for token, rank in (line.split() for line in fid if line)
+        }
+    n_vocab = len(ranks)
+    special_tokens = {}
+
+    specials = [
+        "<|endoftext|>",
+        "<|startoftranscript|>",
+        *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]],
+        "<|translate|>",
+        "<|transcribe|>",
+        "<|startoflm|>",
+        "<|startofprev|>",
+        "<|nospeech|>",
+        "<|notimestamps|>",
+        *[f"<|{i * 0.02:.2f}|>" for i in range(1501)],
+    ]
+
+    for token in specials:
+        special_tokens[token] = n_vocab
+        n_vocab += 1
+
+    return tiktoken.Encoding(
+        name=os.path.basename(vocab_path),
+        explicit_n_vocab=n_vocab,
+        pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
+        mergeable_ranks=ranks,
+        special_tokens=special_tokens,
+    )
+
+
+@lru_cache(maxsize=None)
+def get_tokenizer(
+    multilingual: bool,
+    *,
+    num_languages: int = 99,
+    language: Optional[str] = None,
+    task: Optional[str] = None,  # Literal["transcribe", "translate", None]
+) -> Tokenizer:
+    if language is not None:
+        language = language.lower()
+        if language not in LANGUAGES:
+            if language in TO_LANGUAGE_CODE:
+                language = TO_LANGUAGE_CODE[language]
+            else:
+                raise ValueError(f"Unsupported language: {language}")
+
+    if multilingual:
+        encoding_name = "multilingual"
+        language = language or "en"
+        task = task or "transcribe"
+    else:
+        encoding_name = "gpt2"
+        language = None
+        task = None
+
+    encoding = get_encoding(name=encoding_name, num_languages=num_languages)
+
+    return Tokenizer(
+        encoding=encoding, num_languages=num_languages, language=language, task=task
+    )

whisper/mlx_whisper/torch_whisper.py

@@ -0,0 +1,308 @@
+# Copyright © 2023 Apple Inc.
+
+import base64
+import gzip
+from dataclasses import dataclass
+from typing import Dict, Iterable, Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+
+@dataclass
+class ModelDimensions:
+    n_mels: int
+    n_audio_ctx: int
+    n_audio_state: int
+    n_audio_head: int
+    n_audio_layer: int
+    n_vocab: int
+    n_text_ctx: int
+    n_text_state: int
+    n_text_head: int
+    n_text_layer: int
+
+
+class LayerNorm(nn.LayerNorm):
+    def forward(self, x: Tensor) -> Tensor:
+        return super().forward(x.float()).type(x.dtype)
+
+
+class Linear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        return F.linear(
+            x,
+            self.weight.to(x.dtype),
+            None if self.bias is None else self.bias.to(x.dtype),
+        )
+
+
+class Conv1d(nn.Conv1d):
+    def _conv_forward(
+        self, x: Tensor, weight: Tensor, bias: Optional[Tensor]
+    ) -> Tensor:
+        return super()._conv_forward(
+            x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
+        )
+
+
+def sinusoids(length, channels, max_timescale=10000):
+    """Returns sinusoids for positional embedding"""
+    assert channels % 2 == 0
+    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
+    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, n_state: int, n_head: int):
+        super().__init__()
+        self.n_head = n_head
+        self.query = Linear(n_state, n_state)
+        self.key = Linear(n_state, n_state, bias=False)
+        self.value = Linear(n_state, n_state)
+        self.out = Linear(n_state, n_state)
+
+    def forward(
+        self,
+        x: Tensor,
+        xa: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        kv_cache: Optional[dict] = None,
+    ):
+        q = self.query(x)
+
+        if kv_cache is None or xa is None or self.key not in kv_cache:
+            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
+            # otherwise, perform key/value projections for self- or cross-attention as usual.
+            k = self.key(x if xa is None else xa)
+            v = self.value(x if xa is None else xa)
+        else:
+            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
+            k = kv_cache[self.key]
+            v = kv_cache[self.value]
+
+        wv, qk = self.qkv_attention(q, k, v, mask)
+        return self.out(wv), qk
+
+    def qkv_attention(
+        self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None
+    ):
+        n_batch, n_ctx, n_state = q.shape
+        scale = (n_state // self.n_head) ** -0.25
+        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
+        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
+        v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
+
+        qk = q @ k
+        if mask is not None:
+            qk = qk + mask[:n_ctx, :n_ctx]
+        qk = qk.float()
+
+        w = F.softmax(qk, dim=-1).to(q.dtype)
+        return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
+        super().__init__()
+
+        self.attn = MultiHeadAttention(n_state, n_head)
+        self.attn_ln = LayerNorm(n_state)
+
+        self.cross_attn = (
+            MultiHeadAttention(n_state, n_head) if cross_attention else None
+        )
+        self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
+
+        n_mlp = n_state * 4
+        self.mlp = nn.Sequential(
+            Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)
+        )
+        self.mlp_ln = LayerNorm(n_state)
+
+    def forward(
+        self,
+        x: Tensor,
+        xa: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        kv_cache: Optional[dict] = None,
+    ):
+        x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
+        if self.cross_attn:
+            x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
+        x = x + self.mlp(self.mlp_ln(x))
+        return x
+
+
+class AudioEncoder(nn.Module):
+    def __init__(
+        self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int
+    ):
+        super().__init__()
+        self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
+        self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
+        self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
+
+        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+            [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
+        )
+        self.ln_post = LayerNorm(n_state)
+
+    def forward(self, x: Tensor):
+        """
+        x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
+            the mel spectrogram of the audio
+        """
+        x = F.gelu(self.conv1(x))
+        x = F.gelu(self.conv2(x))
+        x = x.permute(0, 2, 1)
+
+        assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
+        x = (x + self.positional_embedding).to(x.dtype)
+
+        for block in self.blocks:
+            x = block(x)
+
+        x = self.ln_post(x)
+        return x
+
+
+class TextDecoder(nn.Module):
+    def __init__(
+        self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int
+    ):
+        super().__init__()
+
+        self.token_embedding = nn.Embedding(n_vocab, n_state)
+        self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
+
+        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+            [
+                ResidualAttentionBlock(n_state, n_head, cross_attention=True)
+                for _ in range(n_layer)
+            ]
+        )
+        self.ln = LayerNorm(n_state)
+
+        mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
+        self.register_buffer("mask", mask, persistent=False)
+
+    def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
+        """
+        x : torch.LongTensor, shape = (batch_size, <= n_ctx)
+            the text tokens
+        xa : torch.Tensor, shape = (batch_size, n_audio_ctx, n_audio_state)
+            the encoded audio features to be attended on
+        """
+        offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+        x = (
+            self.token_embedding(x)
+            + self.positional_embedding[offset : offset + x.shape[-1]]
+        )
+        x = x.to(xa.dtype)
+
+        for block in self.blocks:
+            x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
+
+        x = self.ln(x)
+        logits = (
+            x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
+        ).float()
+
+        return logits
+
+
+class Whisper(nn.Module):
+    def __init__(self, dims: ModelDimensions):
+        super().__init__()
+        self.dims = dims
+        self.encoder = AudioEncoder(
+            self.dims.n_mels,
+            self.dims.n_audio_ctx,
+            self.dims.n_audio_state,
+            self.dims.n_audio_head,
+            self.dims.n_audio_layer,
+        )
+        self.decoder = TextDecoder(
+            self.dims.n_vocab,
+            self.dims.n_text_ctx,
+            self.dims.n_text_state,
+            self.dims.n_text_head,
+            self.dims.n_text_layer,
+        )
+        # use the last half among the decoder layers for time alignment by default;
+        # to use a specific set of heads, see `set_alignment_heads()` below.
+        all_heads = torch.zeros(
+            self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool
+        )
+        all_heads[self.dims.n_text_layer // 2 :] = True
+        self.register_buffer("alignment_heads", all_heads.to_sparse(), persistent=False)
+
+    def set_alignment_heads(self, dump: bytes):
+        array = np.frombuffer(
+            gzip.decompress(base64.b85decode(dump)), dtype=bool
+        ).copy()
+        mask = torch.from_numpy(array).reshape(
+            self.dims.n_text_layer, self.dims.n_text_head
+        )
+        self.register_buffer("alignment_heads", mask.to_sparse(), persistent=False)
+
+    def embed_audio(self, mel: torch.Tensor):
+        return self.encoder(mel)
+
+    def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
+        return self.decoder(tokens, audio_features)
+
+    def forward(
+        self, mel: torch.Tensor, tokens: torch.Tensor
+    ) -> Dict[str, torch.Tensor]:
+        return self.decoder(tokens, self.encoder(mel))
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @property
+    def is_multilingual(self):
+        return self.dims.n_vocab >= 51865
+
+    @property
+    def num_languages(self):
+        return self.dims.n_vocab - 51765 - int(self.is_multilingual)
+
+    def install_kv_cache_hooks(self, cache: Optional[dict] = None):
+        """
+        The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
+        tensors calculated for the previous positions. This method returns a dictionary that stores
+        all caches, and the necessary hooks for the key and value projection modules that save the
+        intermediate tensors to be reused during later calculations.
+
+        Returns
+        -------
+        cache : Dict[nn.Module, torch.Tensor]
+            A dictionary object mapping the key/value projection modules to its cache
+        hooks : List[RemovableHandle]
+            List of PyTorch RemovableHandle objects to stop the hooks to be called
+        """
+        cache = {**cache} if cache is not None else {}
+        hooks = []
+
+        def save_to_cache(module, _, output):
+            if module not in cache or output.shape[1] > self.dims.n_text_ctx:
+                # save as-is, for the first token or cross attention
+                cache[module] = output
+            else:
+                cache[module] = torch.cat([cache[module], output], dim=1).detach()
+            return cache[module]
+
+        def install_hooks(layer: nn.Module):
+            if isinstance(layer, MultiHeadAttention):
+                hooks.append(layer.key.register_forward_hook(save_to_cache))
+                hooks.append(layer.value.register_forward_hook(save_to_cache))
+
+        self.decoder.apply(install_hooks)
+        return cache, hooks

whisper/mlx_whisper/transcribe.py

@@ -0,0 +1,540 @@
+# Copyright © 2023 Apple Inc.
+
+import sys
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import mlx.core as mx
+import numpy as np
+import tqdm
+
+from .audio import (
+    FRAMES_PER_SECOND,
+    HOP_LENGTH,
+    N_FRAMES,
+    N_SAMPLES,
+    SAMPLE_RATE,
+    log_mel_spectrogram,
+    pad_or_trim,
+)
+from .decoding import DecodingOptions, DecodingResult
+from .load_models import load_model
+from .timing import add_word_timestamps
+from .tokenizer import LANGUAGES, get_tokenizer
+
+
+def _format_timestamp(seconds: float):
+    assert seconds >= 0, "non-negative timestamp expected"
+    milliseconds = round(seconds * 1000.0)
+
+    hours = milliseconds // 3_600_000
+    milliseconds -= hours * 3_600_000
+
+    minutes = milliseconds // 60_000
+    milliseconds -= minutes * 60_000
+
+    seconds = milliseconds // 1_000
+    milliseconds -= seconds * 1_000
+
+    hours_marker = f"{hours:02d}:" if hours > 0 else ""
+    return f"{hours_marker}{minutes:02d}:{seconds:02d}.{milliseconds:03d}"
+
+
+def _get_end(segments: List[dict]) -> Optional[float]:
+    return next(
+        (w["end"] for s in reversed(segments) for w in reversed(s["words"])),
+        segments[-1]["end"] if segments else None,
+    )
+
+
+class ModelHolder:
+    model = None
+    model_path = None
+
+    @classmethod
+    def get_model(cls, model_path: str, dtype: mx.Dtype):
+        if cls.model is None or model_path != cls.model_path:
+            cls.model = load_model(model_path, dtype=dtype)
+            cls.model_path = model_path
+        return cls.model
+
+
+def transcribe(
+    audio: Union[str, np.ndarray, mx.array],
+    *,
+    path_or_hf_repo: str = "mlx-community/whisper-tiny",
+    verbose: Optional[bool] = None,
+    temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
+    compression_ratio_threshold: Optional[float] = 2.4,
+    logprob_threshold: Optional[float] = -1.0,
+    no_speech_threshold: Optional[float] = 0.6,
+    condition_on_previous_text: bool = True,
+    initial_prompt: Optional[str] = None,
+    word_timestamps: bool = False,
+    prepend_punctuations: str = "\"'“¿([{-",
+    append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
+    clip_timestamps: Union[str, List[float]] = "0",
+    hallucination_silence_threshold: Optional[float] = None,
+    **decode_options,
+):
+    """
+    Transcribe an audio file using Whisper
+
+    Parameters
+    ----------
+    audio: Union[str, np.ndarray, mx.array]
+        The path to the audio file to open, or the audio waveform
+
+    path_or_hf_repo: str
+        The localpath to the Whisper model or HF Hub repo with the MLX converted weights.
+
+    verbose: bool
+        Whether to display the text being decoded to the console. If True, displays all the details,
+        If False, displays minimal details. If None, does not display anything
+
+    temperature: Union[float, Tuple[float, ...]]
+        Temperature for sampling. It can be a tuple of temperatures, which will be successively used
+        upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
+
+    compression_ratio_threshold: float
+        If the gzip compression ratio is above this value, treat as failed
+
+    logprob_threshold: float
+        If the average log probability over sampled tokens is below this value, treat as failed
+
+    no_speech_threshold: float
+        If the no_speech probability is higher than this value AND the average log probability
+        over sampled tokens is below `logprob_threshold`, consider the segment as silent
+
+    condition_on_previous_text: bool
+        if True, the previous output of the model is provided as a prompt for the next window;
+        disabling may make the text inconsistent across windows, but the model becomes less prone to
+        getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
+
+    word_timestamps: bool
+        Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
+        and include the timestamps for each word in each segment.
+
+    prepend_punctuations: str
+        If word_timestamps is True, merge these punctuation symbols with the next word
+
+    append_punctuations: str
+        If word_timestamps is True, merge these punctuation symbols with the previous word
+
+    initial_prompt: Optional[str]
+        Optional text to provide as a prompt for the first window. This can be used to provide, or
+        "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
+        to make it more likely to predict those word correctly.
+
+    decode_options: dict
+        Keyword arguments to construct `DecodingOptions` instances
+
+    clip_timestamps: Union[str, List[float]]
+        Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process.
+        The last end timestamp defaults to the end of the file.
+
+    hallucination_silence_threshold: Optional[float]
+        When word_timestamps is True, skip silent periods longer than this threshold (in seconds)
+        when a possible hallucination is detected
+
+    Returns
+    -------
+    A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
+    the spoken language ("language"), which is detected when `decode_options["language"]` is None.
+    """
+
+    dtype = mx.float16 if decode_options.get("fp16", True) else mx.float32
+    model = ModelHolder.get_model(path_or_hf_repo, dtype)
+
+    # Pad 30-seconds of silence to the input audio, for slicing
+    mel = log_mel_spectrogram(audio, n_mels=model.dims.n_mels, padding=N_SAMPLES)
+    content_frames = mel.shape[-2] - N_FRAMES
+    content_duration = float(content_frames * HOP_LENGTH / SAMPLE_RATE)
+
+    if verbose:
+        system_encoding = sys.getdefaultencoding()
+        if system_encoding != "utf-8":
+            make_safe = lambda x: x.encode(system_encoding, errors="replace").decode(
+                system_encoding
+            )
+        else:
+            make_safe = lambda x: x
+
+    if decode_options.get("language", None) is None:
+        if not model.is_multilingual:
+            decode_options["language"] = "en"
+        else:
+            if verbose:
+                print(
+                    "Detecting language using up to the first 30 seconds. "
+                    "Use the `language` decoding option to specify the language"
+                )
+            mel_segment = pad_or_trim(mel, N_FRAMES, axis=-2).astype(dtype)
+            _, probs = model.detect_language(mel_segment)
+            decode_options["language"] = max(probs, key=probs.get)
+            if verbose is not None:
+                print(
+                    f"Detected language: {LANGUAGES[decode_options['language']].title()}"
+                )
+
+    language: str = decode_options["language"]
+    task: str = decode_options.get("task", "transcribe")
+    tokenizer = get_tokenizer(
+        model.is_multilingual,
+        num_languages=model.num_languages,
+        language=language,
+        task=task,
+    )
+
+    if isinstance(clip_timestamps, str):
+        clip_timestamps = [
+            float(ts) for ts in (clip_timestamps.split(",") if clip_timestamps else [])
+        ]
+    seek_points: List[int] = [round(ts * FRAMES_PER_SECOND) for ts in clip_timestamps]
+    if len(seek_points) == 0:
+        seek_points.append(0)
+    if len(seek_points) % 2 == 1:
+        seek_points.append(content_frames)
+    seek_clips: List[Tuple[int, int]] = list(zip(seek_points[::2], seek_points[1::2]))
+
+    punctuation = "\"'“¿([{-\"'.。,，!！?？:：”)]}、"
+
+    if word_timestamps and task == "translate":
+        warnings.warn("Word-level timestamps on translations may not be reliable.")
+
+    def decode_with_fallback(segment: mx.array) -> DecodingResult:
+        temperatures = (
+            [temperature] if isinstance(temperature, (int, float)) else temperature
+        )
+        decode_result = None
+
+        for t in temperatures:
+            kwargs = {**decode_options}
+            if t > 0:
+                # disable beam_size and patience when t > 0
+                kwargs.pop("beam_size", None)
+                kwargs.pop("patience", None)
+            else:
+                # disable best_of when t == 0
+                kwargs.pop("best_of", None)
+
+            options = DecodingOptions(**kwargs, temperature=t)
+            decode_result = model.decode(segment, options)
+
+            needs_fallback = False
+            if (
+                compression_ratio_threshold is not None
+                and decode_result.compression_ratio > compression_ratio_threshold
+            ):
+                needs_fallback = True  # too repetitive
+            if (
+                logprob_threshold is not None
+                and decode_result.avg_logprob < logprob_threshold
+            ):
+                needs_fallback = True  # average log probability is too low
+            if (
+                no_speech_threshold is not None
+                and decode_result.no_speech_prob > no_speech_threshold
+            ):
+                needs_fallback = False  # silence
+            if not needs_fallback:
+                break
+
+        return decode_result
+
+    clip_idx = 0
+    seek = seek_clips[clip_idx][0]
+    input_stride = N_FRAMES // model.dims.n_audio_ctx  # mel frames per output token: 2
+    time_precision = (
+        input_stride * HOP_LENGTH / SAMPLE_RATE
+    )  # time per output token: 0.02 (seconds)
+    all_tokens = []
+    all_segments = []
+    prompt_reset_since = 0
+
+    if initial_prompt is not None:
+        initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
+        all_tokens.extend(initial_prompt_tokens)
+    else:
+        initial_prompt_tokens = []
+
+    def new_segment(
+        *, start: float, end: float, tokens: mx.array, result: DecodingResult
+    ):
+        tokens = tokens.tolist()
+        text_tokens = [token for token in tokens if token < tokenizer.eot]
+        return {
+            "seek": seek,
+            "start": start,
+            "end": end,
+            "text": tokenizer.decode(text_tokens),
+            "tokens": tokens,
+            "temperature": result.temperature,
+            "avg_logprob": result.avg_logprob,
+            "compression_ratio": result.compression_ratio,
+            "no_speech_prob": result.no_speech_prob,
+        }
+
+    # show the progress bar when verbose is False (if True, transcribed text will be printed)
+    with tqdm.tqdm(
+        total=content_frames, unit="frames", disable=verbose is not False
+    ) as pbar:
+        last_speech_timestamp = 0.0
+        for seek_clip_start, seek_clip_end in seek_clips:
+            while seek < seek_clip_end:
+                time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
+                window_end_time = float((seek + N_FRAMES) * HOP_LENGTH / SAMPLE_RATE)
+                segment_size = min(
+                    N_FRAMES, content_frames - seek, seek_clip_end - seek
+                )
+                mel_segment = mel[seek : seek + segment_size]
+                segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
+                mel_segment = pad_or_trim(mel_segment, N_FRAMES, axis=-2).astype(dtype)
+
+                decode_options["prompt"] = all_tokens[prompt_reset_since:]
+                result: DecodingResult = decode_with_fallback(mel_segment)
+                tokens = np.array(result.tokens)
+
+                if no_speech_threshold is not None:
+                    # no voice activity check
+                    should_skip = result.no_speech_prob > no_speech_threshold
+                    if (
+                        logprob_threshold is not None
+                        and result.avg_logprob > logprob_threshold
+                    ):
+                        # don't skip if the logprob is high enough, despite the no_speech_prob
+                        should_skip = False
+
+                    if should_skip:
+                        seek += (
+                            segment_size  # fast-forward to the next segment boundary
+                        )
+                        continue
+
+                previous_seek = seek
+                current_segments = []
+
+                # anomalous words are very long/short/improbable
+                def word_anomaly_score(word: dict) -> float:
+                    probability = word.get("probability", 0.0)
+                    duration = word["end"] - word["start"]
+                    score = 0.0
+                    if probability < 0.15:
+                        score += 1.0
+                    if duration < 0.133:
+                        score += (0.133 - duration) * 15
+                    if duration > 2.0:
+                        score += duration - 2.0
+                    return score
+
+                def is_segment_anomaly(segment: Optional[dict]) -> bool:
+                    if segment is None or not segment["words"]:
+                        return False
+                    words = [
+                        w for w in segment["words"] if w["word"] not in punctuation
+                    ]
+                    words = words[:8]
+                    score = sum(word_anomaly_score(w) for w in words)
+                    return score >= 3 or score + 0.01 >= len(words)
+
+                def next_words_segment(segments: List[dict]) -> Optional[dict]:
+                    return next((s for s in segments if s["words"]), None)
+
+                timestamp_tokens = tokens >= tokenizer.timestamp_begin
+                single_timestamp_ending = timestamp_tokens[-2:].tolist() == [
+                    False,
+                    True,
+                ]
+
+                consecutive = np.where(
+                    np.logical_and(timestamp_tokens[:-1], timestamp_tokens[1:])
+                )[0]
+                consecutive += 1
+                if len(consecutive) > 0:
+                    # if the output contains two consecutive timestamp tokens
+                    slices = consecutive.tolist()
+                    if single_timestamp_ending:
+                        slices.append(len(tokens))
+
+                    last_slice = 0
+                    for current_slice in slices:
+                        sliced_tokens = tokens[last_slice:current_slice]
+                        start_timestamp_pos = (
+                            sliced_tokens[0].item() - tokenizer.timestamp_begin
+                        )
+                        end_timestamp_pos = (
+                            sliced_tokens[-1].item() - tokenizer.timestamp_begin
+                        )
+                        current_segments.append(
+                            new_segment(
+                                start=time_offset
+                                + start_timestamp_pos * time_precision,
+                                end=time_offset + end_timestamp_pos * time_precision,
+                                tokens=sliced_tokens,
+                                result=result,
+                            )
+                        )
+                        last_slice = current_slice
+
+                    if single_timestamp_ending:
+                        # single timestamp at the end means no speech after the last timestamp.
+                        seek += segment_size
+                    else:
+                        # otherwise, ignore the unfinished segment and seek to the last timestamp
+                        last_timestamp_pos = (
+                            tokens[last_slice - 1].item() - tokenizer.timestamp_begin
+                        )
+                        seek += last_timestamp_pos * input_stride
+                else:
+                    duration = segment_duration
+                    timestamps = tokens[timestamp_tokens.nonzero()[0]]
+                    if (
+                        len(timestamps) > 0
+                        and timestamps[-1].item() != tokenizer.timestamp_begin
+                    ):
+                        # no consecutive timestamps but it has a timestamp; use the last one.
+                        last_timestamp_pos = (
+                            timestamps[-1].item() - tokenizer.timestamp_begin
+                        )
+                        duration = last_timestamp_pos * time_precision
+
+                    current_segments.append(
+                        new_segment(
+                            start=time_offset,
+                            end=time_offset + duration,
+                            tokens=tokens,
+                            result=result,
+                        )
+                    )
+                    seek += segment_size
+
+                if word_timestamps:
+                    add_word_timestamps(
+                        segments=current_segments,
+                        model=model,
+                        tokenizer=tokenizer,
+                        mel=mel_segment,
+                        num_frames=segment_size,
+                        prepend_punctuations=prepend_punctuations,
+                        append_punctuations=append_punctuations,
+                        last_speech_timestamp=last_speech_timestamp,
+                    )
+
+                    if not single_timestamp_ending:
+                        last_word_end = _get_end(current_segments)
+                        if last_word_end is not None and last_word_end > time_offset:
+                            seek = round(last_word_end * FRAMES_PER_SECOND)
+
+                    # skip silence before possible hallucinations
+                    if hallucination_silence_threshold is not None:
+                        threshold = hallucination_silence_threshold
+                        if not single_timestamp_ending:
+                            last_word_end = _get_end(current_segments)
+                            if (
+                                last_word_end is not None
+                                and last_word_end > time_offset
+                            ):
+                                remaining_duration = window_end_time - last_word_end
+                                if remaining_duration > threshold:
+                                    seek = round(last_word_end * FRAMES_PER_SECOND)
+                                else:
+                                    seek = previous_seek + segment_size
+
+                        # if first segment might be a hallucination, skip leading silence
+                        first_segment = next_words_segment(current_segments)
+                        if first_segment is not None and is_segment_anomaly(
+                            first_segment
+                        ):
+                            gap = first_segment["start"] - time_offset
+                            if gap > threshold:
+                                seek = previous_seek + round(gap * FRAMES_PER_SECOND)
+                                continue
+
+                        # skip silence before any possible hallucination that is surrounded
+                        # by silence or more hallucinations
+                        hal_last_end = last_speech_timestamp
+                        for si in range(len(current_segments)):
+                            segment = current_segments[si]
+                            if not segment["words"]:
+                                continue
+                            if is_segment_anomaly(segment):
+                                next_segment = next_words_segment(
+                                    current_segments[si + 1 :]
+                                )
+                                if next_segment is not None:
+                                    hal_next_start = next_segment["words"][0]["start"]
+                                else:
+                                    hal_next_start = time_offset + segment_duration
+                                silence_before = (
+                                    segment["start"] - hal_last_end > threshold
+                                    or segment["start"] < threshold
+                                    or segment["start"] - time_offset < 2.0
+                                )
+                                silence_after = (
+                                    hal_next_start - segment["end"] > threshold
+                                    or is_segment_anomaly(next_segment)
+                                    or window_end_time - segment["end"] < 2.0
+                                )
+                                if silence_before and silence_after:
+                                    seek = round(
+                                        max(time_offset + 1, segment["start"])
+                                        * FRAMES_PER_SECOND
+                                    )
+                                    if content_duration - segment["end"] < threshold:
+                                        seek = content_frames
+                                    current_segments[si:] = []
+                                    break
+                            hal_last_end = segment["end"]
+
+                    last_word_end = _get_end(current_segments)
+                    if last_word_end is not None:
+                        last_speech_timestamp = last_word_end
+
+                if verbose:
+                    for segment in current_segments:
+                        start, end, text = (
+                            segment["start"],
+                            segment["end"],
+                            segment["text"],
+                        )
+                        line = f"[{_format_timestamp(start)} --> {_format_timestamp(end)}] {text}"
+                        print(make_safe(line))
+
+                # if a segment is instantaneous or does not contain text, clear it
+                for i, segment in enumerate(current_segments):
+                    if (
+                        segment["start"] == segment["end"]
+                        or segment["text"].strip() == ""
+                    ):
+                        segment["text"] = ""
+                        segment["tokens"] = []
+                        segment["words"] = []
+
+                all_segments.extend(
+                    [
+                        {"id": i, **segment}
+                        for i, segment in enumerate(
+                            current_segments, start=len(all_segments)
+                        )
+                    ]
+                )
+                all_tokens.extend(
+                    [
+                        token
+                        for segment in current_segments
+                        for token in segment["tokens"]
+                    ]
+                )
+
+                if not condition_on_previous_text or result.temperature > 0.5:
+                    # do not feed the prompt tokens if a high temperature was used
+                    prompt_reset_since = len(all_tokens)
+
+                # update progress bar
+                pbar.update(min(content_frames, seek) - previous_seek)
+
+    return dict(
+        text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]),
+        segments=all_segments,
+        language=language,
+    )

whisper/mlx_whisper/version.py

@@ -0,0 +1,3 @@
+# Copyright © 2023-2024 Apple Inc.
+
+__version__ = "0.1.0"

whisper/mlx_whisper/whisper.py

@@ -0,0 +1,267 @@
+# Copyright © 2023 Apple Inc.
+
+import base64
+import gzip
+import math
+from dataclasses import dataclass
+from typing import Union
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+
+from .decoding import decode as decode_function
+from .decoding import detect_language as detect_language_function
+
+
+@dataclass
+class ModelDimensions:
+    n_mels: int
+    n_audio_ctx: int
+    n_audio_state: int
+    n_audio_head: int
+    n_audio_layer: int
+    n_vocab: int
+    n_text_ctx: int
+    n_text_state: int
+    n_text_head: int
+    n_text_layer: int
+
+
+def sinusoids(length, channels, max_timescale=10000):
+    """Returns sinusoids for positional embedding"""
+    assert channels % 2 == 0
+    log_timescale_increment = math.log(max_timescale) / (channels // 2 - 1)
+    inv_timescales = mx.exp(-log_timescale_increment * mx.arange(channels // 2))
+    scaled_time = mx.arange(length)[:, None] * inv_timescales[None, :]
+    return mx.concatenate([mx.sin(scaled_time), mx.cos(scaled_time)], axis=1)
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, n_state: int, n_head: int):
+        super().__init__()
+        self.n_head = n_head
+        self.query = nn.Linear(n_state, n_state)
+        self.key = nn.Linear(n_state, n_state, bias=False)
+        self.value = nn.Linear(n_state, n_state)
+        self.out = nn.Linear(n_state, n_state)
+
+    def __call__(
+        self,
+        x,
+        xa=None,
+        mask=None,
+        kv_cache=None,
+    ):
+        q = self.query(x)
+
+        if xa is None:
+            k = self.key(x)
+            v = self.value(x)
+            if kv_cache is not None:
+                k = mx.concatenate([kv_cache[0], k], axis=1)
+                v = mx.concatenate([kv_cache[1], v], axis=1)
+        elif kv_cache is None:
+            k = self.key(xa)
+            v = self.value(xa)
+        else:
+            k, v = kv_cache
+
+        wv, qk = self.qkv_attention(q, k, v, mask)
+        return self.out(wv), (k, v), qk
+
+    def qkv_attention(self, q, k, v, mask=None):
+        n_batch, n_ctx, n_state = q.shape
+        scale = (n_state // self.n_head) ** -0.25
+        q = q.reshape(*q.shape[:2], self.n_head, -1).transpose(0, 2, 1, 3) * scale
+        k = k.reshape(*k.shape[:2], self.n_head, -1).transpose(0, 2, 3, 1) * scale
+        v = v.reshape(*v.shape[:2], self.n_head, -1).transpose(0, 2, 1, 3)
+
+        qk = q @ k
+        if mask is not None:
+            qk = qk + mask[:n_ctx, :n_ctx]
+        qk = qk.astype(mx.float32)
+
+        w = mx.softmax(qk, axis=-1).astype(q.dtype)
+        out = (w @ v).transpose(0, 2, 1, 3)
+        out = out.reshape(n_batch, n_ctx, n_state)
+        return out, qk
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
+        super().__init__()
+
+        self.attn = MultiHeadAttention(n_state, n_head)
+        self.attn_ln = nn.LayerNorm(n_state)
+
+        self.cross_attn = (
+            MultiHeadAttention(n_state, n_head) if cross_attention else None
+        )
+        self.cross_attn_ln = nn.LayerNorm(n_state) if cross_attention else None
+
+        n_mlp = n_state * 4
+        self.mlp1 = nn.Linear(n_state, n_mlp)
+        self.mlp2 = nn.Linear(n_mlp, n_state)
+        self.mlp_ln = nn.LayerNorm(n_state)
+
+    def __call__(self, x, xa=None, mask=None, kv_cache=None):
+        kv, cross_kv = kv_cache if kv_cache else (None, None)
+        y, kv, _ = self.attn(self.attn_ln(x), mask=mask, kv_cache=kv)
+        x += y
+        cross_qk = None
+        if self.cross_attn:
+            y, cross_kv, cross_qk = self.cross_attn(
+                self.cross_attn_ln(x), xa, kv_cache=cross_kv
+            )
+            x += y
+        x = x + self.mlp2(nn.gelu(self.mlp1(self.mlp_ln(x))))
+        return x, (kv, cross_kv), cross_qk
+
+
+class AudioEncoder(nn.Module):
+    def __init__(
+        self,
+        n_mels: int,
+        n_ctx: int,
+        n_state: int,
+        n_head: int,
+        n_layer: int,
+        dtype: mx.Dtype = mx.float16,
+    ):
+        super().__init__()
+        self.conv1 = nn.Conv1d(n_mels, n_state, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
+        self._positional_embedding = sinusoids(n_ctx, n_state).astype(dtype)
+
+        self.blocks = [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
+        self.ln_post = nn.LayerNorm(n_state)
+
+    def __call__(self, x):
+        x = nn.gelu(self.conv1(x))
+        x = nn.gelu(self.conv2(x))
+        assert x.shape[1:] == self._positional_embedding.shape, "incorrect audio shape"
+        x = x + self._positional_embedding
+
+        for block in self.blocks:
+            x, _, _ = block(x)
+
+        x = self.ln_post(x)
+        return x
+
+
+class TextDecoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab: int,
+        n_ctx: int,
+        n_state: int,
+        n_head: int,
+        n_layer: int,
+        dtype: mx.Dtype = mx.float16,
+    ):
+        super().__init__()
+
+        self.token_embedding = nn.Embedding(n_vocab, n_state)
+        self.positional_embedding = mx.zeros((n_ctx, n_state))
+
+        self.blocks = [
+            ResidualAttentionBlock(n_state, n_head, cross_attention=True)
+            for _ in range(n_layer)
+        ]
+        self.ln = nn.LayerNorm(n_state)
+        self._mask = nn.MultiHeadAttention.create_additive_causal_mask(n_ctx).astype(
+            dtype
+        )
+
+    def __call__(self, x, xa, kv_cache=None):
+        """
+        x : mx.array, shape = (batch_size, <= n_ctx)
+            the text tokens
+        xa : mx.array, shape = (batch_size, n_audio_ctx, n_audio_state)
+            the encoded audio features to be attended on
+        """
+        offset = kv_cache[0][0][0].shape[1] if kv_cache else 0
+        x = (
+            self.token_embedding(x)
+            + self.positional_embedding[offset : offset + x.shape[-1]]
+        )
+
+        if kv_cache is None:
+            kv_cache = [None] * len(self.blocks)
+        cross_qk = [None] * len(self.blocks)
+        for e, block in enumerate(self.blocks):
+            x, kv_cache[e], cross_qk[e] = block(
+                x, xa, mask=self._mask, kv_cache=kv_cache[e]
+            )
+
+        x = self.ln(x)
+        return self.token_embedding.as_linear(x), kv_cache, cross_qk
+
+
+class Whisper(nn.Module):
+    def __init__(self, dims: ModelDimensions, dtype: mx.Dtype = mx.float16):
+        super().__init__()
+        self.dims = dims
+        self.encoder = AudioEncoder(
+            self.dims.n_mels,
+            self.dims.n_audio_ctx,
+            self.dims.n_audio_state,
+            self.dims.n_audio_head,
+            self.dims.n_audio_layer,
+            dtype,
+        )
+        self.decoder = TextDecoder(
+            self.dims.n_vocab,
+            self.dims.n_text_ctx,
+            self.dims.n_text_state,
+            self.dims.n_text_head,
+            self.dims.n_text_layer,
+            dtype,
+        )
+        # use the last half among the decoder layers for time alignment by default;
+        # to use a specific set of heads, see `set_alignment_heads()` below.
+        all_heads = np.zeros(
+            (self.dims.n_text_layer, self.dims.n_text_head), dtype=bool
+        )
+        all_heads[self.dims.n_text_layer // 2 :] = True
+        self.alignment_heads = mx.array(np.asarray(all_heads.nonzero()).T)
+
+    def set_alignment_heads(self, dump: Union[bytes, np.ndarray]):
+        if isinstance(dump, np.ndarray):
+            self.alignment_heads = mx.array(dump)
+        elif isinstance(dump, bytes):
+            array = np.frombuffer(
+                gzip.decompress(base64.b85decode(dump)), dtype=bool
+            ).copy()
+            mask = array.reshape(self.dims.n_text_layer, self.dims.n_text_head)
+            self.alignment_heads = mx.array(np.asarray(mask.nonzero()).T)
+        else:
+            raise ValueError(
+                f"Invalid type for `dump`: {type(dump)}. Expected a np.ndarray or base85-encoded bytes containing"
+                " alignment_head information"
+            )
+
+    def embed_audio(self, mel):
+        return self.encoder(mel)
+
+    def logits(self, tokens, audio_features):
+        return self.decoder(tokens, audio_features)[0]
+
+    def forward_with_cross_qk(self, mel, tokens):
+        logits, _, cross_qk = self.decoder(tokens, self.encoder(mel))
+        return logits, cross_qk
+
+    def __call__(self, mel, tokens):
+        return self.decoder(tokens, self.encoder(mel))[0]
+
+    @property
+    def is_multilingual(self):
+        return self.dims.n_vocab >= 51865
+
+    @property
+    def num_languages(self):
+        return self.dims.n_vocab - 51765 - int(self.is_multilingual)
+
+    detect_language = detect_language_function
+    decode = decode_function