feat(whisper): support whisper arch #2141
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Looking forward to the |
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Confirm that import torchaudio
import numpy as np
from subprocess import CalledProcessError, run
wav_file = "BAC009S0724W0121.wav"
# 1. torchaudio
waveform_torchaudio, sample_rate = torchaudio.load(wav_file)
waveform_torchaudio = waveform_torchaudio.numpy().flatten().astype(np.float32)
# 2. whisper
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 np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
wavform_whisper = load_audio(wav_file)
# 3. compare
print(waveform_torchaudio.shape, waveform_torchaudio[:10])
print(wavform_whisper.shape, wavform_whisper[:10])
print(np.allclose(waveform_torchaudio, wavform_whisper))
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fbank这一块需要改动吗 |
正在验证的就是这个 |
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Compared to import torch
import torchaudio
import librosa
import numpy as np
import torch.nn.functional as F
import torchaudio.transforms as T
from functools import lru_cache
from typing import Optional, Union
from subprocess import CalledProcessError, run
wav_file = "BAC009S0724W0121.wav"
N_MEL = 128
SAMPLE_RATE = 16000
N_FFT = 400
HOP_LENGTH = 160
WINDOW_LENGTH = N_FFT
# 1. torchaudio
waveform_torchaudio, sample_rate = torchaudio.load(wav_file)
def torchaudio_log_mel_spectrogram(
audio: torch.Tensor,
n_mels: int = 80,
padding: int = 0,
device: Optional[Union[str, torch.device]] = None,
):
if device is not None:
audio = audio.to(device)
if padding > 0:
audio = F.pad(audio, (0, padding))
window = torch.hann_window(N_FFT).to(audio.device)
stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
magnitudes = stft[..., :-1].abs() ** 2
mel_transform = T.MelScale(
n_mels=N_MEL,
sample_rate=SAMPLE_RATE,
n_stft=(N_FFT // 2) + 1,
norm="slaney",
mel_scale="slaney"
)
mel_spec = mel_transform(magnitudes).squeeze(0)
log_spec = torch.clamp(mel_spec, min=1e-10).log10()
log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
log_spec = (log_spec + 4.0) / 4.0
return log_spec
mat_torchaudio = torchaudio_log_mel_spectrogram(waveform_torchaudio).numpy()
# 2. librosa
def librosa_log_mel_spectrogram(
audio: torch.Tensor,
n_mels: int = 80,
padding: int = 0,
device: Optional[Union[str, torch.device]] = None,
):
if device is not None:
audio = audio.to(device)
if padding > 0:
audio = F.pad(audio, (0, padding))
window = torch.hann_window(N_FFT).to(audio.device)
stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
magnitudes = stft[..., :-1].abs() ** 2
filters = torch.from_numpy(
librosa.filters.mel(sr=SAMPLE_RATE, n_fft=N_FFT, n_mels=N_MEL)
).to(magnitudes.device)
mel_spec = filters @ magnitudes
log_spec = torch.clamp(mel_spec, min=1e-10).log10().squeeze(0)
log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
log_spec = (log_spec + 4.0) / 4.0
return log_spec
mat_librosa = librosa_log_mel_spectrogram(waveform_torchaudio).numpy()
# 3. whisper
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 np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
@lru_cache(maxsize=None)
def mel_filters(device, n_mels: int) -> torch.Tensor:
"""
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}"
filters_path = "mel_filters.npz"
with np.load(filters_path, allow_pickle=False) as f:
return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
def log_mel_spectrogram(
audio: Union[str, np.ndarray, torch.Tensor],
n_mels: int = 80,
padding: int = 0,
device: Optional[Union[str, torch.device]] = None,
):
"""
Compute the log-Mel spectrogram of
Parameters
----------
audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
The path to audio or either a NumPy array or Tensor
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
device: Optional[Union[str, torch.device]]
If given, the audio tensor is moved to this device before STFT
Returns
-------
torch.Tensor, shape = (80, n_frames)
A Tensor that contains the Mel spectrogram
"""
if not torch.is_tensor(audio):
if isinstance(audio, str):
audio = load_audio(audio)
audio = torch.from_numpy(audio)
if device is not None:
audio = audio.to(device)
if padding > 0:
audio = F.pad(audio, (0, padding))
window = torch.hann_window(N_FFT).to(audio.device)
stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
magnitudes = stft[..., :-1].abs() ** 2
filters = mel_filters(audio.device, n_mels)
mel_spec = filters @ magnitudes
log_spec = torch.clamp(mel_spec, min=1e-10).log10()
log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
log_spec = (log_spec + 4.0) / 4.0
return log_spec
mat_whisper = log_mel_spectrogram(
wav_file, n_mels=N_MEL, padding=0, device="cpu").numpy()
# 3. compare
print("torchaudio\n", mat_torchaudio.shape, mat_torchaudio[:10])
print("librosa\n", mat_librosa.shape, mat_librosa[:10])
print("whisper\n", mat_whisper.shape, mat_whisper[:10])
print("=================== librosa v.s. whisper =====================")
print("librosa v.s. whisper", np.allclose(mat_librosa, mat_whisper, atol=1e-06))
np.testing.assert_allclose(mat_librosa, mat_whisper, atol=1e-06)
print("=================== torchaudio v.s. whisper =====================")
print("torchaudio v.s. whisper", np.allclose(mat_torchaudio, mat_whisper, atol=1e-04))
np.testing.assert_allclose(mat_torchaudio, mat_whisper, atol=1e-04)
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confirm that #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright [2023-11-15] <[email protected], Xingchen Song>
import torch
import math
import numpy as np
def wenet_sinusoids(length, channels):
"""Returns sinusoids for positional embedding"""
d_model = channels
xscale = math.sqrt(d_model)
max_len = length
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len,
dtype=torch.float32).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2, dtype=torch.float32) *
-(math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
return pe
def whisper_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)
wenet_pe = wenet_sinusoids(100, 512).numpy()
whisper_pe = whisper_sinusoids(100, 512).numpy()
print(wenet_pe.shape)
print(whisper_pe.shape)
np.testing.assert_allclose(wenet_pe, whisper_pe, atol=1e-8)
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load checkpoint succeed：
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有个提议，直接把
whisper的requirments和我们的基本不冲突
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model 也要用whisper仓库的吗？ |
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引入whisper的库只用他的log_mel_spec和tokenizer，引入这个库，那啥mel_filter.npz和xx.tiktoken也不用手动下载了。cli也可以复用他的download相关函数来下载ckpt |
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pass training |
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whisper-style decoder input:
def add_whisper_tokens(
tokenizer, ys_pad: torch.Tensor,
ignore_id: int, task_id: int, no_timestamp: bool,
language: str, use_prev: bool
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Add whisper-style tokens.
([PREV] -> [previous text tokens or hotwords]).optional --
┌------------------------------------------------------↲
↓
[sot] -> [language id] -> [transcribe] -> [begin time] -> [text tokens] -> [end time] -> ... -> [eot] # noqa
| | |-------> [no timestamps] -> [text tokens] ----------------------↑ # noqa
| | | # noqa
| |--------> [translate] -> [begin time] -> [text tokens] -> [end time] -> ... --->| # noqa
| |-------> [no timestamps] -> [text tokens] --------------------->| # noqa
| | # noqa
|--> [no speech(VAD)] ---------------------------------------------------------------------->| # noqa
Args:
tokenizer: get IDs of special tokens
ignore_id (int): index of padding
no_timestamp (bool): whether to add timestamps tokens
language (str): language tag
Returns:
ys_in (torch.Tensor) : (B, Lmax + ?)
ys_out (torch.Tensor) : (B, Lmax + ?)
"""
if use_prev:
# i.e., hotword list
_prev = [tokenizer.sot_prev]
# append hotword list to _prev
# ...
raise NotImplementedError
else:
_prev = []
language_id = tokenizer.sot + 1 + WHISPER_LANGS.index(language)
_sot = _prev + [tokenizer.sot, language_id, task_id]
_eot = torch.tensor([tokenizer.eot],
dtype=torch.long,
requires_grad=False,
device=ys_pad.device)
ys = [y[y != ignore_id] for y in ys_pad] # parse padded ys
if task_id == tokenizer.transcribe or task_id == tokenizer.translate:
if no_timestamp:
_sot.append(tokenizer.no_timestamps)
else:
_sot.append(tokenizer.timestamp_begin)
# add subsequent tokens
# ...
raise NotImplementedError
elif task_id == tokenizer.no_speech:
_sot.append(tokenizer.no_speech)
else:
raise NotImplementedError
_sot = torch.tensor(_sot, dtype=torch.long,
requires_grad=False, device=ys_pad.device)
ys_in = [torch.cat([_sot, y], dim=0) for y in ys]
ys_out = [torch.cat([_sot[1:], y, _eot], dim=0) for y in ys]
return pad_list(ys_in, tokenizer.eot), pad_list(ys_out, ignore_id) |
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增加了unit test (单元测试包含
从 softmax 后的数值可以看出，概率归一化之后，应该不影响解码结果 |
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pass unit test, ready for final review @Mddct @robin1001 @whiteshirt0429 p.s. 现在单元测试大概需要7~8min |
xingchensong
changed the title
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From binbin: |
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robin1001
reviewed
Nov 22, 2023
robin1001
approved these changes
Nov 22, 2023
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一些开源工具在集成 whisper 时的相关实现（供参考）：
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finetuinng on aishell (u2 transformer, enc&dec init from whisper, ctc init randomly) seems work (torch-ddp in red, deepspeed-stage1 in blue)
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指定 |
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whisper 潜在问题，whisper的encoder和decoder均采用固定长度emb（encoder 30s， decoder 448字符），因此当开启speed_perturb，变速1.1时 30s的audio可能会造成assertion error #2171 |
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whisper的tokenizer 和 中文常用的char tokenizer，效率对比如下： #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright [2023-11-30] <[email protected], Xingchen Song>
import statistics as s
from whisper.tokenizer import get_tokenizer
tokenizer = get_tokenizer(multilingual=True, num_languages=100)
char_lens, token_lens = [], []
with open("data/train/text", "r") as f:
lines = f.readlines()
for l in lines:
l = l.strip().split()[1]
char_len = len(l)
token_len = len(tokenizer.encoding.encode(l))
char_lens.append(char_len)
token_lens.append(token_len)
print("{} {} {}".format(char_len, token_len, l))
print("Mean: CharTokenizer {}, WhisperTokenizer {}".format(s.mean(char_lens),
s.mean(token_lens)))
print("Var : CharTokenizer {}, WhisperTokenizer {}".format(s.variance(char_lens),
s.variance(token_lens)))...
16 21 他还增设了一种叫消防费的收费项目
10 11 总共收上来一四万馀元
8 9 都揣进了自己腰包
20 32 改装小货车撞上出租车钢筋将出租车后座顶出
12 14 八月二零日晚将近一零点钟
15 23 一辆改装的小货车撞上一辆出租车
15 25 货车装载的钢筋将出租车后座对穿
9 12 幸亏车后座没有乘客
13 20 钢筋穿过后排座位并顶出车外
11 11 案件性质关系到国家安全
Mean: CharTokenizer 14.405843561091775, WhisperTokenizer 17.837832436843243
Var : CharTokenizer 18.59714879629654, WhisperTokenizer 35.86092595601854 |
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whisper的tokenizer和英文常用的bpe tokenizer，效率对比如下： #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright [2023-11-30] <[email protected], Xingchen Song>
import statistics as s
import sentencepiece as spm
from whisper.tokenizer import get_tokenizer
from wenet.text.tokenize_utils import tokenize_by_bpe_model
tokenizer = get_tokenizer(multilingual=True, num_languages=100)
bpe_model = spm.SentencePieceProcessor()
bpe_model.load("../../../test/resources/librispeech.train_960_unigram5000.bpemodel")
bpe_lens, token_lens = [], []
with open("data/test_clean/text", "r") as f:
lines = f.readlines()
for l in lines:
l = " ".join(l.strip().split()[1:])
bpe_len = len(tokenize_by_bpe_model(bpe_model, l))
token_len = len(tokenizer.encoding.encode(l))
bpe_lens.append(bpe_len)
token_lens.append(token_len)
print("{} {} {}".format(bpe_len, token_len, l))
print("Mean: BpeTokenizer {}, WhisperTokenizer {}".format(s.mean(bpe_lens),
s.mean(token_lens)))
print("Var : BpeTokenizer {}, WhisperTokenizer {}".format(s.variance(bpe_lens),
s.variance(token_lens)))...
18 25 I THANK ALL WHO HAVE LOVED ME IN THEIR HEARTS WITH THANKS AND LOVE FROM MINE
36 62 THEN I LONG TRIED BY NATURAL ILLS RECEIVED THE COMFORT FAST WHILE BUDDING AT THY SIGHT MY PILGRIM'S STAFF GAVE OUT GREEN LEAVES WITH MORNING DEWS IMPEARLED
24 32 I LOVE THEE FREELY AS MEN STRIVE FOR RIGHT I LOVE THEE PURELY AS THEY TURN FROM PRAISE
22 34 I LOVE THEE WITH THE PASSION PUT TO USE IN MY OLD GRIEFS AND WITH MY CHILDHOOD'S FAITH
43 58 I LOVE THEE WITH A LOVE I SEEMED TO LOSE WITH MY LOST SAINTS I LOVE THEE WITH THE BREATH SMILES TEARS OF ALL MY LIFE AND IF GOD CHOOSE I SHALL BUT LOVE THEE BETTER AFTER DEATH
Mean: BpeTokenizer 25.176335877862595, WhisperTokenizer 40.09809160305343
Var : BpeTokenizer 314.1025325790101, WhisperTokenizer 814.1969417556378
... |
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The training of a 1.5 billion parameter model places high demands on CPU memory, and issues like the following are likely to occur on systems with memory equal to or less than 160GB. |
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whisper基本结构约等于wenet.transformer，复用大部分代码+修改ckpt命名 而不是直接将 openai-whisper的模型定义copy过来，是出于以下几点考虑：
TODO (This PR)
TODO (Next PR)