mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 01:17:28 +08:00
7d7e236061
- bert/model.py:10: tree_unflatten - bert/model.py:2: dataclass - bert/model.py:8: numpy - cifar/resnet.py:6: Any - clip/model.py:15: tree_flatten - clip/model.py:9: Union - gcn/main.py:8: download_cora - gcn/main.py:9: cross_entropy - llms/gguf_llm/models.py:12: tree_flatten, tree_unflatten - llms/gguf_llm/models.py:9: numpy - llms/mixtral/mixtral.py:12: tree_map - llms/mlx_lm/models/dbrx.py:2: Dict, Union - llms/mlx_lm/tuner/trainer.py:5: partial - llms/speculative_decoding/decoder.py:1: dataclass, field - llms/speculative_decoding/decoder.py:2: Optional - llms/speculative_decoding/decoder.py:5: mlx.nn - llms/speculative_decoding/decoder.py:6: numpy - llms/speculative_decoding/main.py:2: glob - llms/speculative_decoding/main.py:3: json - llms/speculative_decoding/main.py:5: Path - llms/speculative_decoding/main.py:8: mlx.nn - llms/speculative_decoding/model.py:6: tree_unflatten - llms/speculative_decoding/model.py:7: AutoTokenizer - llms/tests/test_lora.py:13: yaml_loader - lora/lora.py:14: tree_unflatten - lora/models.py:11: numpy - lora/models.py:3: glob - speechcommands/kwt.py:1: Any - speechcommands/main.py:7: mlx.data - stable_diffusion/stable_diffusion/model_io.py:4: partial - whisper/benchmark.py:5: sys - whisper/test.py:5: subprocess - whisper/whisper/audio.py:6: Optional - whisper/whisper/decoding.py:8: mlx.nn
466 lines
14 KiB
Python
466 lines
14 KiB
Python
# Copyright © 2023 Apple Inc.
|
|
|
|
import json
|
|
import os
|
|
import unittest
|
|
from dataclasses import asdict
|
|
from pathlib import Path
|
|
|
|
import mlx.core as mx
|
|
import numpy as np
|
|
import torch
|
|
from convert import load_torch_model, quantize, torch_to_mlx
|
|
from mlx.utils import tree_flatten
|
|
|
|
import whisper
|
|
import whisper.audio as audio
|
|
import whisper.decoding as decoding
|
|
import whisper.load_models as load_models
|
|
|
|
MODEL_NAME = "tiny"
|
|
MLX_FP32_MODEL_PATH = "mlx_models/tiny_fp32"
|
|
MLX_FP16_MODEL_PATH = "mlx_models/tiny_fp16"
|
|
MLX_4BITS_MODEL_PATH = "mlx_models/tiny_quantized_4bits"
|
|
TEST_AUDIO = "whisper/assets/ls_test.flac"
|
|
|
|
|
|
def _save_model(save_dir, weights, config):
|
|
mlx_path = Path(save_dir)
|
|
mlx_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
# Save weights
|
|
np.savez(str(mlx_path / "weights.npz"), **weights)
|
|
|
|
# Save config.json with model_type
|
|
with open(str(mlx_path / "config.json"), "w") as f:
|
|
config["model_type"] = "whisper"
|
|
json.dump(config, f, indent=4)
|
|
|
|
config.pop("model_type", None)
|
|
|
|
|
|
def load_torch_and_mlx():
|
|
torch_model = load_torch_model(MODEL_NAME)
|
|
|
|
fp32_model = torch_to_mlx(torch_model, dtype=mx.float32)
|
|
config = asdict(fp32_model.dims)
|
|
weights = dict(tree_flatten(fp32_model.parameters()))
|
|
_save_model(MLX_FP32_MODEL_PATH, weights, config)
|
|
|
|
fp16_model = torch_to_mlx(torch_model, dtype=mx.float16)
|
|
config = asdict(fp16_model.dims)
|
|
weights = dict(tree_flatten(fp16_model.parameters()))
|
|
_save_model(MLX_FP16_MODEL_PATH, weights, config)
|
|
|
|
args = type("", (), {})()
|
|
args.q_group_size = 64
|
|
args.q_bits = 4
|
|
weights, config = quantize(weights, config, args)
|
|
_save_model(MLX_4BITS_MODEL_PATH, weights, config)
|
|
|
|
return torch_model, fp32_model, fp16_model
|
|
|
|
|
|
def forward_torch(model, mels, tokens):
|
|
mels = torch.Tensor(mels).to(torch.float32)
|
|
tokens = torch.Tensor(tokens).to(torch.int32)
|
|
with torch.no_grad():
|
|
logits = model.forward(mels, tokens)
|
|
return logits.numpy()
|
|
|
|
|
|
def forward_mlx(model, mels, tokens):
|
|
mels = mx.array(mels.transpose(0, 2, 1))
|
|
tokens = mx.array(tokens, mx.int32)
|
|
logits = model(mels, tokens)
|
|
return np.array(logits)
|
|
|
|
|
|
class TestWhisper(unittest.TestCase):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
_, cls.model, _ = load_torch_and_mlx()
|
|
data = audio.load_audio(TEST_AUDIO)
|
|
data = audio.pad_or_trim(data)
|
|
cls.mels = audio.log_mel_spectrogram(data)
|
|
|
|
def test_torch_mlx(self):
|
|
np.random.seed(10)
|
|
|
|
torch_model = load_torch_model(MODEL_NAME)
|
|
dims = torch_model.dims
|
|
|
|
mels = np.random.randn(1, dims.n_mels, 3_000)
|
|
tokens = np.random.randint(0, dims.n_vocab, (1, 20))
|
|
|
|
torch_logits = forward_torch(torch_model, mels, tokens)
|
|
|
|
mlx_logits = forward_mlx(self.model, mels, tokens)
|
|
|
|
self.assertTrue(np.allclose(torch_logits, mlx_logits, atol=1e-2, rtol=1e-2))
|
|
|
|
def test_fp16(self):
|
|
mlx_model = load_models.load_model(MLX_FP16_MODEL_PATH, mx.float16)
|
|
dims = mlx_model.dims
|
|
mels = mx.array(np.random.randn(1, 3_000, dims.n_mels), mx.float16)
|
|
tokens = mx.array(np.random.randint(0, dims.n_vocab, (1, 20)), mx.int32)
|
|
logits = mlx_model(mels, tokens)
|
|
self.assertEqual(logits.dtype, mx.float16)
|
|
|
|
def test_quantized_4bits(self):
|
|
mlx_model = load_models.load_model(MLX_4BITS_MODEL_PATH, mx.float16)
|
|
dims = mlx_model.dims
|
|
mels = mx.array(np.random.randn(1, 3_000, dims.n_mels), mx.float16)
|
|
tokens = mx.array(np.random.randint(0, dims.n_vocab, (1, 20)), mx.int32)
|
|
logits = mlx_model(mels, tokens)
|
|
# Here, we just test if 4-bit models can forward, as the quantized tiny models struggle with accurate transcription
|
|
self.assertEqual(logits.dtype, mx.float16)
|
|
|
|
def test_decode_lang(self):
|
|
options = decoding.DecodingOptions(task="lang_id", fp16=False)
|
|
result = decoding.decode(self.model, self.mels, options)
|
|
self.assertEqual(result.language, "en")
|
|
self.assertEqual(len(result.language_probs), 99)
|
|
self.assertAlmostEqual(
|
|
result.language_probs["en"], 0.9947282671928406, places=5
|
|
)
|
|
|
|
def test_decode_greedy(self):
|
|
result = decoding.decode(self.model, self.mels, fp16=False)
|
|
self.assertEqual(result.language, "en")
|
|
self.assertEqual(
|
|
result.tokens,
|
|
[
|
|
50364,
|
|
1396,
|
|
264,
|
|
665,
|
|
5133,
|
|
23109,
|
|
25462,
|
|
264,
|
|
6582,
|
|
293,
|
|
750,
|
|
632,
|
|
42841,
|
|
292,
|
|
370,
|
|
938,
|
|
294,
|
|
4054,
|
|
293,
|
|
12653,
|
|
356,
|
|
50620,
|
|
50620,
|
|
23563,
|
|
322,
|
|
3312,
|
|
13,
|
|
50680,
|
|
],
|
|
)
|
|
self.assertEqual(
|
|
result.text,
|
|
(
|
|
"Then the good soul openly sorted the boat and she "
|
|
"had buoyed so long in secret and bravely stretched on alone."
|
|
),
|
|
)
|
|
self.assertAlmostEqual(result.avg_logprob, -0.4975455382774616, places=3)
|
|
self.assertAlmostEqual(result.no_speech_prob, 0.009631240740418434, places=4)
|
|
self.assertAlmostEqual(result.compression_ratio, 1.2359550561797752)
|
|
|
|
# Small temp should give the same results
|
|
result = decoding.decode(self.model, self.mels, temperature=1e-8, fp16=False)
|
|
|
|
self.assertEqual(
|
|
result.text,
|
|
(
|
|
"Then the good soul openly sorted the boat and she "
|
|
"had buoyed so long in secret and bravely stretched on alone."
|
|
),
|
|
)
|
|
self.assertAlmostEqual(result.avg_logprob, -0.4975455382774616, places=3)
|
|
self.assertAlmostEqual(result.no_speech_prob, 0.009631240740418434, places=4)
|
|
self.assertAlmostEqual(result.compression_ratio, 1.2359550561797752)
|
|
|
|
def test_transcribe(self):
|
|
result = whisper.transcribe(
|
|
TEST_AUDIO, path_or_hf_repo=MLX_FP32_MODEL_PATH, fp16=False
|
|
)
|
|
self.assertEqual(
|
|
result["text"],
|
|
(
|
|
" Then the good soul openly sorted the boat and she "
|
|
"had buoyed so long in secret and bravely stretched on alone."
|
|
),
|
|
)
|
|
|
|
def test_transcribe_alice(self):
|
|
audio_file = os.path.join(
|
|
os.path.expanduser("~"),
|
|
".cache/whisper/alice.mp3",
|
|
)
|
|
if not os.path.exists(audio_file):
|
|
print("To run this test download the alice in wonderland audiobook:")
|
|
print("bash path_to_whisper_repo/whisper/assets/download_alice.sh")
|
|
return
|
|
|
|
result = whisper.transcribe(
|
|
audio_file, path_or_hf_repo=MLX_FP32_MODEL_PATH, fp16=False
|
|
)
|
|
self.assertEqual(len(result["text"]), 10920)
|
|
self.assertEqual(result["language"], "en")
|
|
self.assertEqual(len(result["segments"]), 77)
|
|
|
|
expected_5 = {
|
|
"id": 5,
|
|
"seek": 2800,
|
|
"start": 40.0,
|
|
"end": 46.0,
|
|
"text": " Oh my poor little feet, I wonder who will put on your shoes and stockings for you now tears.",
|
|
"tokens": [
|
|
50964,
|
|
876,
|
|
452,
|
|
4716,
|
|
707,
|
|
3521,
|
|
11,
|
|
286,
|
|
2441,
|
|
567,
|
|
486,
|
|
829,
|
|
322,
|
|
428,
|
|
6654,
|
|
293,
|
|
4127,
|
|
1109,
|
|
337,
|
|
291,
|
|
586,
|
|
10462,
|
|
13,
|
|
51264,
|
|
],
|
|
"temperature": 0.0,
|
|
"avg_logprob": -0.19670599699020386,
|
|
"compression_ratio": 1.5991379310344827,
|
|
"no_speech_prob": 0.09746722131967545,
|
|
}
|
|
|
|
expected_73 = {
|
|
"id": 73,
|
|
"seek": 70700,
|
|
"start": 707.0,
|
|
"end": 715.0,
|
|
"text": " let us get to the shore, and then I'll tell you my history, and you'll understand why it is that I hate cats and dogs.",
|
|
"tokens": [
|
|
50364,
|
|
718,
|
|
505,
|
|
483,
|
|
281,
|
|
264,
|
|
17805,
|
|
11,
|
|
293,
|
|
550,
|
|
286,
|
|
603,
|
|
980,
|
|
291,
|
|
452,
|
|
2503,
|
|
11,
|
|
293,
|
|
291,
|
|
603,
|
|
1223,
|
|
983,
|
|
309,
|
|
307,
|
|
300,
|
|
286,
|
|
4700,
|
|
11111,
|
|
293,
|
|
7197,
|
|
13,
|
|
50764,
|
|
],
|
|
"temperature": 0.0,
|
|
"avg_logprob": -0.1350895343440594,
|
|
"compression_ratio": 1.6208333333333333,
|
|
"no_speech_prob": 0.009053784422576427,
|
|
}
|
|
|
|
def check_segment(seg, expected):
|
|
for k, v in expected.items():
|
|
if isinstance(v, float):
|
|
self.assertAlmostEqual(seg[k], v, places=2)
|
|
else:
|
|
self.assertEqual(seg[k], v)
|
|
|
|
# Randomly check a couple of segments
|
|
check_segment(result["segments"][5], expected_5)
|
|
check_segment(result["segments"][73], expected_73)
|
|
|
|
def test_transcribe_word_level_timestamps_confidence_scores(self):
|
|
result = whisper.transcribe(
|
|
TEST_AUDIO,
|
|
path_or_hf_repo=MLX_FP16_MODEL_PATH,
|
|
word_timestamps=True,
|
|
)
|
|
|
|
# result predicted with openai-whisper
|
|
expected_0 = [
|
|
{
|
|
"word": " Then",
|
|
"start": 0.0,
|
|
"end": 0.94,
|
|
"probability": 0.855542778968811,
|
|
},
|
|
{
|
|
"word": " the",
|
|
"start": 0.94,
|
|
"end": 1.12,
|
|
"probability": 0.6500106453895569,
|
|
},
|
|
{
|
|
"word": " good",
|
|
"start": 1.12,
|
|
"end": 1.32,
|
|
"probability": 0.5503873825073242,
|
|
},
|
|
{
|
|
"word": " soul",
|
|
"start": 1.32,
|
|
"end": 1.56,
|
|
"probability": 0.46757155656814575,
|
|
},
|
|
{
|
|
"word": " openly",
|
|
"start": 1.56,
|
|
"end": 2.0,
|
|
"probability": 0.9840946793556213,
|
|
},
|
|
{
|
|
"word": " sorted",
|
|
"start": 2.0,
|
|
"end": 2.38,
|
|
"probability": 0.24167272448539734,
|
|
},
|
|
{
|
|
"word": " the",
|
|
"start": 2.38,
|
|
"end": 2.58,
|
|
"probability": 0.9875414967536926,
|
|
},
|
|
{
|
|
"word": " boat",
|
|
"start": 2.58,
|
|
"end": 2.8,
|
|
"probability": 0.5856029391288757,
|
|
},
|
|
{
|
|
"word": " and",
|
|
"start": 2.8,
|
|
"end": 2.98,
|
|
"probability": 0.913351833820343,
|
|
},
|
|
{
|
|
"word": " she",
|
|
"start": 2.98,
|
|
"end": 3.1,
|
|
"probability": 0.9913808703422546,
|
|
},
|
|
{
|
|
"word": " had",
|
|
"start": 3.1,
|
|
"end": 3.32,
|
|
"probability": 0.9952940344810486,
|
|
},
|
|
{
|
|
"word": " buoyed",
|
|
"start": 3.32,
|
|
"end": 3.58,
|
|
"probability": 0.6411589980125427,
|
|
},
|
|
{
|
|
"word": " so",
|
|
"start": 3.58,
|
|
"end": 3.8,
|
|
"probability": 0.9682658314704895,
|
|
},
|
|
{
|
|
"word": " long",
|
|
"start": 3.8,
|
|
"end": 4.06,
|
|
"probability": 0.9953522682189941,
|
|
},
|
|
{
|
|
"word": " in",
|
|
"start": 4.06,
|
|
"end": 4.26,
|
|
"probability": 0.6745936870574951,
|
|
},
|
|
{
|
|
"word": " secret",
|
|
"start": 4.26,
|
|
"end": 4.56,
|
|
"probability": 0.9905064702033997,
|
|
},
|
|
{
|
|
"word": " and",
|
|
"start": 4.56,
|
|
"end": 4.9,
|
|
"probability": 0.856008768081665,
|
|
},
|
|
{
|
|
"word": " bravely",
|
|
"start": 4.9,
|
|
"end": 5.28,
|
|
"probability": 0.8477402329444885,
|
|
},
|
|
]
|
|
|
|
def check_words(words, expected_words):
|
|
for word, expected_word in zip(words, expected_words):
|
|
for k, v in expected_word.items():
|
|
if isinstance(v, float):
|
|
self.assertAlmostEqual(word[k], v, places=1)
|
|
else:
|
|
self.assertEqual(word[k], v)
|
|
|
|
# Randomly check a couple of segments
|
|
check_words(result["segments"][0]["words"], expected_0)
|
|
|
|
|
|
class TestAudio(unittest.TestCase):
|
|
def test_load(self):
|
|
data = audio.load_audio(TEST_AUDIO)
|
|
data_8k = audio.load_audio(TEST_AUDIO, 8000)
|
|
n = 106640
|
|
self.assertTrue(data.shape, (n,))
|
|
self.assertTrue(data.dtype, np.float32)
|
|
self.assertTrue(data_8k.shape, (n // 2,))
|
|
|
|
def test_pad(self):
|
|
data = audio.load_audio(TEST_AUDIO)
|
|
data = audio.pad_or_trim(data, 20_000)
|
|
self.assertTrue(data.shape, [20_000])
|
|
|
|
def test_mel_spec(self):
|
|
mels = audio.log_mel_spectrogram(TEST_AUDIO)
|
|
self.assertTrue(mels.shape, [80, 400])
|
|
self.assertTrue(mels.dtype, mx.float32)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|