mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 09:21:18 +08:00
cleanup whisper a little (#639)
This commit is contained in:
Awni Hannun
GitHub
parent
f6283ef7ce
commit
78c431dc25
@ -239,12 +239,13 @@ def generate(
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),
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range(max_tokens),
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):
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if token == tokenizer.eos_token_id:
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break
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token = token.item()
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if n == 0:
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prompt_time = time.perf_counter() - tic
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tic = time.perf_counter()
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tokens.append(token.item())
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if token == tokenizer.eos_token_id:
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break
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tokens.append(token)
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if verbose:
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s = tokenizer.decode(tokens)
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@ -91,7 +91,8 @@ def _download(url: str, root: str) -> str:
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output.write(buffer)
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loop.update(len(buffer))
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model_bytes = open(download_target, "rb").read()
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with open(download_target, "rb") as fid:
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model_bytes = fid.read()
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if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
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raise RuntimeError(
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"Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."
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@ -297,7 +297,7 @@ class TestWhisper(unittest.TestCase):
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"temperature": 0.0,
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"avg_logprob": -0.1350895343440594,
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"compression_ratio": 1.6208333333333333,
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"no_speech_prob": 0.002246702555567026,
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"no_speech_prob": 0.009053784422576427,
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}
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def check_segment(seg, expected):
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@ -58,7 +58,7 @@ def load_audio(file: str, sr: int = SAMPLE_RATE):
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except CalledProcessError as e:
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raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
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return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
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return mx.array(np.frombuffer(out, np.int16)).flatten().astype(mx.float32) / 32768.0
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def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
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@ -73,8 +73,7 @@ def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
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if array.shape[axis] < length:
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pad_widths = [(0, 0)] * array.ndim
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pad_widths[axis] = (0, length - array.shape[axis])
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pad_fn = mx.pad if isinstance(array, mx.array) else np.pad
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array = pad_fn(array, pad_widths)
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array = mx.pad(array, pad_widths)
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return array
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@ -154,9 +153,9 @@ def log_mel_spectrogram(
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"""
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device = mx.default_device()
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mx.set_default_device(mx.cpu)
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if not isinstance(audio, mx.array):
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if isinstance(audio, str):
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audio = load_audio(audio)
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if isinstance(audio, str):
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audio = load_audio(audio)
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elif not isinstance(audio, mx.array):
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audio = mx.array(audio)
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if padding > 0:
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@ -280,243 +280,258 @@ def transcribe(
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total=content_frames, unit="frames", disable=verbose is not False
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) as pbar:
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last_speech_timestamp = 0.0
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# NOTE: This loop is obscurely flattened to make the diff readable.
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# A later commit should turn this into a simpler nested loop.
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# for seek_clip_start, seek_clip_end in seek_clips:
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# while seek < seek_clip_end
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while clip_idx < len(seek_clips):
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seek_clip_start, seek_clip_end = seek_clips[clip_idx]
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if seek < seek_clip_start:
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seek = seek_clip_start
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if seek >= seek_clip_end:
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clip_idx += 1
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if clip_idx < len(seek_clips):
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seek = seek_clips[clip_idx][0]
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continue
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time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
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window_end_time = float((seek + N_FRAMES) * HOP_LENGTH / SAMPLE_RATE)
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segment_size = min(N_FRAMES, content_frames - seek, seek_clip_end - seek)
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mel_segment = mel[seek : seek + segment_size]
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segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
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mel_segment = pad_or_trim(mel_segment, N_FRAMES, axis=-2).astype(dtype)
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for seek_clip_start, seek_clip_end in seek_clips:
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while seek < seek_clip_end:
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time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
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window_end_time = float((seek + N_FRAMES) * HOP_LENGTH / SAMPLE_RATE)
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segment_size = min(
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N_FRAMES, content_frames - seek, seek_clip_end - seek
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)
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mel_segment = mel[seek : seek + segment_size]
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segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
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mel_segment = pad_or_trim(mel_segment, N_FRAMES, axis=-2).astype(dtype)
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decode_options["prompt"] = all_tokens[prompt_reset_since:]
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result: DecodingResult = decode_with_fallback(mel_segment)
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tokens = np.array(result.tokens)
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decode_options["prompt"] = all_tokens[prompt_reset_since:]
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result: DecodingResult = decode_with_fallback(mel_segment)
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tokens = np.array(result.tokens)
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if no_speech_threshold is not None:
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# no voice activity check
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should_skip = result.no_speech_prob > no_speech_threshold
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if (
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logprob_threshold is not None
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and result.avg_logprob > logprob_threshold
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):
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# don't skip if the logprob is high enough, despite the no_speech_prob
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should_skip = False
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if no_speech_threshold is not None:
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# no voice activity check
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should_skip = result.no_speech_prob > no_speech_threshold
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if (
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logprob_threshold is not None
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and result.avg_logprob > logprob_threshold
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):
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# don't skip if the logprob is high enough, despite the no_speech_prob
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should_skip = False
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if should_skip:
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seek += segment_size # fast-forward to the next segment boundary
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continue
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if should_skip:
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seek += (
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segment_size # fast-forward to the next segment boundary
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)
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continue
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previous_seek = seek
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current_segments = []
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previous_seek = seek
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current_segments = []
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# anomalous words are very long/short/improbable
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def word_anomaly_score(word: dict) -> float:
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probability = word.get("probability", 0.0)
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duration = word["end"] - word["start"]
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score = 0.0
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if probability < 0.15:
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score += 1.0
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if duration < 0.133:
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score += (0.133 - duration) * 15
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if duration > 2.0:
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score += duration - 2.0
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return score
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# anomalous words are very long/short/improbable
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def word_anomaly_score(word: dict) -> float:
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probability = word.get("probability", 0.0)
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duration = word["end"] - word["start"]
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score = 0.0
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if probability < 0.15:
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score += 1.0
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if duration < 0.133:
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score += (0.133 - duration) * 15
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if duration > 2.0:
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score += duration - 2.0
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return score
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def is_segment_anomaly(segment: Optional[dict]) -> bool:
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if segment is None or not segment["words"]:
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return False
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words = [w for w in segment["words"] if w["word"] not in punctuation]
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words = words[:8]
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score = sum(word_anomaly_score(w) for w in words)
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return score >= 3 or score + 0.01 >= len(words)
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def is_segment_anomaly(segment: Optional[dict]) -> bool:
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if segment is None or not segment["words"]:
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return False
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words = [
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w for w in segment["words"] if w["word"] not in punctuation
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]
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words = words[:8]
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score = sum(word_anomaly_score(w) for w in words)
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return score >= 3 or score + 0.01 >= len(words)
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def next_words_segment(segments: List[dict]) -> Optional[dict]:
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return next((s for s in segments if s["words"]), None)
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def next_words_segment(segments: List[dict]) -> Optional[dict]:
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return next((s for s in segments if s["words"]), None)
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timestamp_tokens = tokens >= tokenizer.timestamp_begin
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single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True]
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timestamp_tokens = tokens >= tokenizer.timestamp_begin
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single_timestamp_ending = timestamp_tokens[-2:].tolist() == [
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False,
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True,
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]
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consecutive = np.where(
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np.logical_and(timestamp_tokens[:-1], timestamp_tokens[1:])
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)[0]
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consecutive += 1
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if len(consecutive) > 0:
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# if the output contains two consecutive timestamp tokens
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slices = consecutive.tolist()
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if single_timestamp_ending:
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slices.append(len(tokens))
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consecutive = np.where(
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np.logical_and(timestamp_tokens[:-1], timestamp_tokens[1:])
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)[0]
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consecutive += 1
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if len(consecutive) > 0:
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# if the output contains two consecutive timestamp tokens
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slices = consecutive.tolist()
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if single_timestamp_ending:
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slices.append(len(tokens))
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last_slice = 0
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for current_slice in slices:
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sliced_tokens = tokens[last_slice:current_slice]
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start_timestamp_pos = (
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sliced_tokens[0].item() - tokenizer.timestamp_begin
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)
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end_timestamp_pos = (
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sliced_tokens[-1].item() - tokenizer.timestamp_begin
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)
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current_segments.append(
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new_segment(
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start=time_offset
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+ start_timestamp_pos * time_precision,
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end=time_offset + end_timestamp_pos * time_precision,
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tokens=sliced_tokens,
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result=result,
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)
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)
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last_slice = current_slice
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if single_timestamp_ending:
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# single timestamp at the end means no speech after the last timestamp.
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seek += segment_size
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else:
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# otherwise, ignore the unfinished segment and seek to the last timestamp
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last_timestamp_pos = (
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tokens[last_slice - 1].item() - tokenizer.timestamp_begin
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)
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seek += last_timestamp_pos * input_stride
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else:
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duration = segment_duration
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timestamps = tokens[timestamp_tokens.nonzero()[0]]
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if (
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len(timestamps) > 0
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and timestamps[-1].item() != tokenizer.timestamp_begin
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):
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# no consecutive timestamps but it has a timestamp; use the last one.
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last_timestamp_pos = (
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timestamps[-1].item() - tokenizer.timestamp_begin
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)
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duration = last_timestamp_pos * time_precision
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last_slice = 0
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for current_slice in slices:
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sliced_tokens = tokens[last_slice:current_slice]
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start_timestamp_pos = (
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sliced_tokens[0].item() - tokenizer.timestamp_begin
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)
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end_timestamp_pos = (
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sliced_tokens[-1].item() - tokenizer.timestamp_begin
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)
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current_segments.append(
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new_segment(
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start=time_offset + start_timestamp_pos * time_precision,
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end=time_offset + end_timestamp_pos * time_precision,
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tokens=sliced_tokens,
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start=time_offset,
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end=time_offset + duration,
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tokens=tokens,
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result=result,
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)
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)
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last_slice = current_slice
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if single_timestamp_ending:
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# single timestamp at the end means no speech after the last timestamp.
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seek += segment_size
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else:
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# otherwise, ignore the unfinished segment and seek to the last timestamp
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last_timestamp_pos = (
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tokens[last_slice - 1].item() - tokenizer.timestamp_begin
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if word_timestamps:
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add_word_timestamps(
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segments=current_segments,
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model=model,
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tokenizer=tokenizer,
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mel=mel_segment,
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num_frames=segment_size,
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prepend_punctuations=prepend_punctuations,
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append_punctuations=append_punctuations,
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last_speech_timestamp=last_speech_timestamp,
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)
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seek += last_timestamp_pos * input_stride
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else:
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duration = segment_duration
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timestamps = tokens[timestamp_tokens.nonzero()[0]]
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if (
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len(timestamps) > 0
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and timestamps[-1].item() != tokenizer.timestamp_begin
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):
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# no consecutive timestamps but it has a timestamp; use the last one.
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last_timestamp_pos = (
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timestamps[-1].item() - tokenizer.timestamp_begin
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)
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duration = last_timestamp_pos * time_precision
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current_segments.append(
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new_segment(
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start=time_offset,
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end=time_offset + duration,
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tokens=tokens,
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result=result,
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)
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)
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seek += segment_size
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if word_timestamps:
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add_word_timestamps(
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segments=current_segments,
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model=model,
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tokenizer=tokenizer,
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mel=mel_segment,
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num_frames=segment_size,
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prepend_punctuations=prepend_punctuations,
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append_punctuations=append_punctuations,
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last_speech_timestamp=last_speech_timestamp,
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)
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if not single_timestamp_ending:
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last_word_end = _get_end(current_segments)
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if last_word_end is not None and last_word_end > time_offset:
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seek = round(last_word_end * FRAMES_PER_SECOND)
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# skip silence before possible hallucinations
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if hallucination_silence_threshold is not None:
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threshold = hallucination_silence_threshold
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if not single_timestamp_ending:
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last_word_end = _get_end(current_segments)
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if last_word_end is not None and last_word_end > time_offset:
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remaining_duration = window_end_time - last_word_end
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if remaining_duration > threshold:
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seek = round(last_word_end * FRAMES_PER_SECOND)
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else:
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seek = previous_seek + segment_size
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seek = round(last_word_end * FRAMES_PER_SECOND)
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# if first segment might be a hallucination, skip leading silence
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first_segment = next_words_segment(current_segments)
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if first_segment is not None and is_segment_anomaly(first_segment):
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gap = first_segment["start"] - time_offset
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if gap > threshold:
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seek = previous_seek + round(gap * FRAMES_PER_SECOND)
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continue
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# skip silence before possible hallucinations
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if hallucination_silence_threshold is not None:
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threshold = hallucination_silence_threshold
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if not single_timestamp_ending:
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last_word_end = _get_end(current_segments)
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if (
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last_word_end is not None
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and last_word_end > time_offset
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):
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remaining_duration = window_end_time - last_word_end
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if remaining_duration > threshold:
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seek = round(last_word_end * FRAMES_PER_SECOND)
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else:
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seek = previous_seek + segment_size
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# skip silence before any possible hallucination that is surrounded
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# by silence or more hallucinations
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hal_last_end = last_speech_timestamp
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for si in range(len(current_segments)):
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segment = current_segments[si]
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if not segment["words"]:
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continue
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if is_segment_anomaly(segment):
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next_segment = next_words_segment(
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current_segments[si + 1 :]
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)
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if next_segment is not None:
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hal_next_start = next_segment["words"][0]["start"]
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else:
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hal_next_start = time_offset + segment_duration
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silence_before = (
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segment["start"] - hal_last_end > threshold
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or segment["start"] < threshold
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or segment["start"] - time_offset < 2.0
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)
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silence_after = (
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hal_next_start - segment["end"] > threshold
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or is_segment_anomaly(next_segment)
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or window_end_time - segment["end"] < 2.0
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)
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if silence_before and silence_after:
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seek = round(
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max(time_offset + 1, segment["start"])
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* FRAMES_PER_SECOND
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# if first segment might be a hallucination, skip leading silence
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first_segment = next_words_segment(current_segments)
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if first_segment is not None and is_segment_anomaly(
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first_segment
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):
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gap = first_segment["start"] - time_offset
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if gap > threshold:
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seek = previous_seek + round(gap * FRAMES_PER_SECOND)
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continue
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# skip silence before any possible hallucination that is surrounded
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# by silence or more hallucinations
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hal_last_end = last_speech_timestamp
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for si in range(len(current_segments)):
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segment = current_segments[si]
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if not segment["words"]:
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continue
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if is_segment_anomaly(segment):
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next_segment = next_words_segment(
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current_segments[si + 1 :]
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)
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if content_duration - segment["end"] < threshold:
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seek = content_frames
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current_segments[si:] = []
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break
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hal_last_end = segment["end"]
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if next_segment is not None:
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hal_next_start = next_segment["words"][0]["start"]
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else:
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hal_next_start = time_offset + segment_duration
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silence_before = (
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segment["start"] - hal_last_end > threshold
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or segment["start"] < threshold
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or segment["start"] - time_offset < 2.0
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)
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silence_after = (
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hal_next_start - segment["end"] > threshold
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or is_segment_anomaly(next_segment)
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or window_end_time - segment["end"] < 2.0
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)
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if silence_before and silence_after:
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seek = round(
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max(time_offset + 1, segment["start"])
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* FRAMES_PER_SECOND
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||||
)
|
||||
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
|
||||
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 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"] = []
|
||||
# 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"]]
|
||||
)
|
||||
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)
|
||||
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)
|
||||
# update progress bar
|
||||
pbar.update(min(content_frames, seek) - previous_seek)
|
||||
|
||||
return dict(
|
||||
text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]),
|
||||
|
||||
@ -115,7 +115,7 @@ class ResidualAttentionBlock(nn.Module):
|
||||
self.cross_attn_ln(x), xa, kv_cache=cross_kv
|
||||
)
|
||||
x += y
|
||||
x = x + self.mlp2(nn.gelu(self.mlp1(self.mlp_ln(x))).astype(x.dtype))
|
||||
x = x + self.mlp2(nn.gelu(self.mlp1(self.mlp_ln(x))))
|
||||
return x, (kv, cross_kv), cross_qk
|
||||
|
||||
|
||||
@ -138,8 +138,8 @@ class AudioEncoder(nn.Module):
|
||||
self.ln_post = nn.LayerNorm(n_state)
|
||||
|
||||
def __call__(self, x):
|
||||
x = nn.gelu(self.conv1(x)).astype(x.dtype)
|
||||
x = nn.gelu(self.conv2(x)).astype(x.dtype)
|
||||
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
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user