branch: master
whisper.py
16962 bytesRaw
# thanks to https://github.com/openai/whisper for a good chunk of MIT licensed code
import sys, base64, multiprocessing, itertools, collections
from typing import Optional, Union, Literal, List
from tinygrad import Tensor, TinyJit, Variable, nn
from tinygrad.nn.state import torch_load, load_state_dict
from tinygrad.helpers import getenv, DEBUG, fetch
import numpy as np
import librosa
class MultiHeadAttention:
def __init__(self, n_state, n_head, kv_caching: Literal['cross', 'self']=None, max_self_attn_cache_len=None):
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)
self.kv_caching = kv_caching
self.max_self_attn_cache_len = max_self_attn_cache_len
def __call__(self, x:Tensor, xa:Optional[Tensor]=None, mask:Optional[Tensor]=None, len: Union[Variable,int]=None):
if self.kv_caching == 'cross':
if xa is not None:
k, v = self.key(xa), self.value(xa)
if not hasattr(self, 'cache_k'):
self.cache_k, self.cache_v = k, v
else:
self.cache_k.assign(k).realize()
self.cache_v.assign(v).realize()
else:
k, v = self.cache_k, self.cache_v
else:
k, v = self.key(x), self.value(x)
if self.kv_caching == 'self':
if not hasattr(self, 'cache_k'):
self.cache_k = Tensor.zeros(x.shape[0], self.max_self_attn_cache_len, x.shape[2])
self.cache_v = Tensor.zeros(x.shape[0], self.max_self_attn_cache_len, x.shape[2])
k = self.cache_k.shrink((None, (0, len), None)).cat(k, dim=1)
v = self.cache_v.shrink((None, (0, len), None)).cat(v, dim=1)
padding = self.max_self_attn_cache_len-len-x.shape[1]
self.cache_k.assign(k.pad((None, (0, padding), None)).contiguous()).realize()
self.cache_v.assign(v.pad((None, (0, padding), None)).contiguous()).realize()
q = self.query(x)
n_ctx = q.shape[1]
assert(q.shape[-1] == k.shape[-1] == v.shape[-1])
head_dim = q.shape[-1] // self.n_head
q = q.reshape(*q.shape[:2], self.n_head, head_dim).permute(0, 2, 1, 3)
k = k.reshape(*k.shape[:2], self.n_head, head_dim).permute(0, 2, 1, 3)
v = v.reshape(*v.shape[:2], self.n_head, head_dim).permute(0, 2, 1, 3)
attn = Tensor.scaled_dot_product_attention(q, k, v, mask[:n_ctx,:n_ctx] if mask is not None else None)
wv = attn.permute(0, 2, 1, 3).flatten(start_dim=2)
return self.out(wv)
class ResidualAttentionBlock:
def __init__(self, n_state, n_head, is_decoder_block=False, max_self_attn_cache_len=None):
self.attn = MultiHeadAttention(n_state, n_head, kv_caching='self' if is_decoder_block else None, max_self_attn_cache_len=max_self_attn_cache_len)
self.attn_ln = nn.LayerNorm(n_state)
self.cross_attn = MultiHeadAttention(n_state, n_head, kv_caching='cross') if is_decoder_block else None
self.cross_attn_ln = nn.LayerNorm(n_state) if is_decoder_block else None
self.mlp = [nn.Linear(n_state, n_state*4), Tensor.gelu, nn.Linear(n_state*4, n_state)]
self.mlp_ln = nn.LayerNorm(n_state)
def __call__(self, x, xa=None, mask=None, len: Union[Variable, int]=None):
x = x + self.attn(self.attn_ln(x), mask=mask, len=len)
if self.cross_attn: x = x + self.cross_attn(self.cross_attn_ln(x), xa)
x = x + self.mlp_ln(x).sequential(self.mlp)
return x.realize()
class AudioEncoder:
def __init__(self, n_mels, n_audio_ctx, n_audio_state, n_audio_head, n_audio_layer, **_):
self.conv1 = nn.Conv1d(n_mels, n_audio_state, kernel_size=3, padding=1)
self.conv2 = nn.Conv1d(n_audio_state, n_audio_state, kernel_size=3, stride=2, padding=1)
self.blocks = [ResidualAttentionBlock(n_audio_state, n_audio_head) for _ in range(n_audio_layer)]
self.ln_post = nn.LayerNorm(n_audio_state)
self.positional_embedding = Tensor.empty(n_audio_ctx, n_audio_state)
self.encode = TinyJit(self.__call__)
def __call__(self, x):
x = self.conv1(x).gelu()
x = self.conv2(x).gelu()
x = x.permute(0, 2, 1)
x = x + self.positional_embedding[:x.shape[1]]
x = x.sequential(self.blocks)
x = self.ln_post(x)
return x.realize()
class TextDecoder:
def __init__(self, n_vocab, n_text_ctx, n_text_state, n_text_head, n_text_layer, **_):
self.max_tokens_to_sample = n_text_ctx // 2
self.max_self_attn_cache_len = self.max_tokens_to_sample * 2 + 5 # roughly prompt + start toks + max_tokens_to_sample
self.token_embedding = nn.Embedding(n_vocab, n_text_state)
self.positional_embedding = Tensor.empty(n_text_ctx, n_text_state)
self.blocks = [ResidualAttentionBlock(n_text_state, n_text_head, is_decoder_block=True, max_self_attn_cache_len=self.max_self_attn_cache_len) for _ in range(n_text_layer)]
self.ln = nn.LayerNorm(n_text_state)
self.mask = Tensor.full((n_text_ctx, n_text_ctx), -np.inf).triu(1).realize()
self.getjitted = collections.defaultdict(lambda: TinyJit(self.forward))
def __call__(self, x: Tensor, pos: int, encoded_audio: Tensor):
pos = Variable("self_attn_cache_len", 1, self.max_self_attn_cache_len).bind(pos) if pos else 0
return self.getjitted[x.shape](x, pos, encoded_audio)
def forward(self, x:Tensor, pos:Union[Variable, Literal[0]], encoded_audio:Tensor):
seqlen = x.shape[-1]
x = self.token_embedding(x) + self.positional_embedding.shrink(((pos, pos+seqlen), None, None))
for block in self.blocks: x = block(x, xa=encoded_audio, mask=self.mask, len=pos)
return self.output_tok(x)
def output_tok(self, x):
return (self.ln(x) @ self.token_embedding.weight.T).realize()
class Whisper:
def __init__(self, dims, batch_size=1):
self.encoder = AudioEncoder(**dims)
self.decoder = TextDecoder(**dims)
self.is_multilingual = dims["n_vocab"] == 51865
self.batch_size = batch_size
RATE = 16000
SEGMENT_SECONDS=30
SAMPLES_PER_SEGMENT = RATE * SEGMENT_SECONDS # 480000
N_FFT = 400
HOP_LENGTH = 160
N_MELS = 80
FRAMES_PER_SEGMENT = SAMPLES_PER_SEGMENT // HOP_LENGTH # 3000
def prep_audio(waveforms: List[np.ndarray], batch_size: int, truncate=False) -> np.ndarray:
"""
:param waveforms: A list of possibly variable length 16000Hz audio samples
:param batch_size: The batch_size associated with the Whisper model being used to transcribe the audio.
Used to prevent JIT mismatch errors since the encoder does not accept symbolic shapes
:param truncate: If true, truncates (or pads) audio to exactly 30s for a single encoder pass
:return: mel spectrogram of the given waveforms
"""
def pad_or_trim(arr, target_len):
curr_len = len(arr)
if curr_len == target_len:
return arr
elif curr_len < target_len:
return np.pad(arr, (0, target_len - curr_len), 'constant')
else:
return arr[:target_len]
max_len = SAMPLES_PER_SEGMENT if truncate else max(len(wav) for wav in waveforms)
if (r := max_len % SAMPLES_PER_SEGMENT) > 0: max_len += SAMPLES_PER_SEGMENT - r
waveforms = np.array(list(map(lambda w: pad_or_trim(w, max_len), waveforms)))
assert waveforms.shape[0] <= batch_size
if waveforms.shape[0] < batch_size:
# we could have a symbolic batch_size dim instead of manually padding here if conv/layernorm supported symbolic shapes
waveforms = np.pad(waveforms, pad_width=((0, batch_size - waveforms.shape[0]), (0, 0)))
stft = librosa.stft(waveforms, n_fft=N_FFT, hop_length=HOP_LENGTH, window='hann', dtype=np.csingle)
magnitudes = np.absolute(stft[..., :-1]) ** 2
mel_spec = librosa.filters.mel(sr=RATE, n_fft=N_FFT, n_mels=N_MELS) @ magnitudes
log_spec = np.log10(np.clip(mel_spec, 1e-10, None))
log_spec = np.maximum(log_spec, log_spec.max((1,2), keepdims=True) - 8.0)
log_spec = (log_spec + 4.0) / 4.0
return log_spec
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",
}
def get_encoding(encoding_name):
with fetch(f"https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/{encoding_name}.tiktoken").open() as f:
ranks = {base64.b64decode(token): int(rank) for token, rank in (line.split() for line in f if line)}
n_vocab = len(ranks)
specials = [
"<|endoftext|>",
"<|startoftranscript|>",
*[f"<|{lang}|>" for lang in LANGUAGES.keys()],
"<|translate|>",
"<|transcribe|>",
"<|startoflm|>",
"<|startofprev|>",
"<|nospeech|>",
"<|notimestamps|>",
*[f"<|{i * 0.02:.2f}|>" for i in range(1501)],
]
special_tokens = dict(zip(specials, itertools.count(n_vocab)))
n_vocab += len(specials)
import tiktoken
return tiktoken.Encoding(
name=encoding_name,
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)
MODEL_URLS = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def init_whisper(model_name="tiny.en", batch_size=1):
assert MODEL_URLS[model_name] is not None
filename = fetch(MODEL_URLS[model_name])
state = torch_load(filename)
model = Whisper(state['dims'], batch_size)
load_state_dict(model, state['model_state_dict'], strict=False)
enc = get_encoding("multilingual" if model.is_multilingual else "gpt2")
return model, enc
def load_file_waveform(filename):
waveform, _ = librosa.load(filename, sr=RATE)
return waveform
def transcribe_file(model, enc, filename):
return transcribe_waveform(model, enc, [load_file_waveform(filename)])
def transcribe_waveform(model: Whisper, enc, waveforms, truncate=False):
"""
Expects an array of shape (N,S) where N is the number waveforms to transcribe in parallel and S is number of 16000Hz samples
Returns the transcribed text if a single waveform is provided, or an array of transcriptions if multiple are provided
"""
log_spec = prep_audio(waveforms, model.batch_size, truncate)
nsample = model.decoder.max_tokens_to_sample
def inferloop(ctx: Union[np.ndarray, List[np.ndarray]], encoded_audio):
pos, next_tokens = 0, ctx
for i in range((nsample-len(start_tokens))*2):
next_tokens = model.decoder(Tensor(next_tokens), pos, encoded_audio)[:, -1].argmax(axis=-1).numpy().astype(np.int32).reshape(-1, 1)
next_tokens[ctx[:, -1] == eot] = eot
ctx = np.concatenate((ctx, next_tokens), axis=1)
pos = ctx.shape[-1] - 1
if (next_tokens == eot).all(): break
return ctx
def gettexttoks(line): return [tok for tok in line if tok < eot or tok > enc._special_tokens["<|notimestamps|>"]][-nsample+len(start_tokens):]
start_tokens = [enc._special_tokens["<|startoftranscript|>"]]
if model.is_multilingual:
# TODO detect language
language_token = enc._special_tokens["<|startoftranscript|>"] + 1 + tuple(LANGUAGES.keys()).index("en")
start_tokens.append(language_token)
start_tokens.append(enc._special_tokens["<|transcribe|>"])
start_tokens.append(enc._special_tokens["<|notimestamps|>"])
eot = enc._special_tokens["<|endoftext|>"]
ctx = np.tile(start_tokens, (model.batch_size,1))
transcriptions = [[] for _ in waveforms]
for curr_frame in range(0, log_spec.shape[-1], FRAMES_PER_SEGMENT):
encoded_audio = model.encoder.encode(Tensor(log_spec[:, :, curr_frame:curr_frame + FRAMES_PER_SEGMENT]))
if all(len(c) == len(ctx[0]) for c in ctx): ctx = inferloop(np.array(ctx), encoded_audio)
else: ctx = [inferloop((np.array([c]*model.batch_size)), encoded_audio)[i] for i,c in enumerate(ctx)]
for i, (res, arr) in enumerate(zip(transcriptions, ctx)):
if curr_frame*HOP_LENGTH <= len(waveforms[i]):res.extend(arr[np.where(arr == start_tokens[-1])[0][0]+1:eoti[0] if len (eoti:=np.where(arr == eot)[0]) else None])
ctx = [[enc._special_tokens['<|startofprev|>']]+gettexttoks(cs)+start_tokens for cs in ctx]
transcriptions = list(map(lambda tokens: enc.decode(tokens).strip(), transcriptions))
return transcriptions if len(transcriptions) > 1 else transcriptions[0]
CHUNK = 1600
RECORD_SECONDS = 10
def listener(q):
import pyaudio
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16, channels=1, rate=RATE, input=True, frames_per_buffer=CHUNK)
print("listening")
for _ in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
data = stream.read(CHUNK)
waveform = ((np.frombuffer(data, np.int16)/32768).astype(np.float32)*3)
q.put(waveform)
print("done listening")
if __name__ == "__main__":
model, enc = init_whisper("small.en" if getenv("SMALL") else "tiny.en", batch_size=1)
if len(sys.argv) > 1:
print(transcribe_file(model, enc, sys.argv[1]))
else:
# online
q = multiprocessing.Queue()
p = multiprocessing.Process(target=listener, args=(q,))
p.daemon = True
p.start()
lst = [enc._special_tokens["<|startoftranscript|>"], enc._special_tokens["<|notimestamps|>"]]
total = None
did_read = False
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
while not q.empty() or total is None:
waveform = q.get()
if total is None: total = waveform
else: total = np.concatenate([total, waveform])
did_read = True
if did_read:
log_spec = prep_audio(total.reshape(1, -1), model.batch_size, truncate=True)
encoded_audio = model.encoder.encode(Tensor(log_spec))
# pass the previously inferred tokens as 'prefix' - https://github.com/openai/whisper/discussions/117#discussioncomment-3727051
out = model.decoder(Tensor([lst]), 0, encoded_audio, streaming=True).realize()
idx = int(out[0,-1].argmax().numpy().item())
lst.append(idx)
dec = enc.decode(lst)
print(dec) # DO NOT REMOVE PRINT. IT'S VERY IMPORTANT
if dec.endswith("<|endoftext|>"):
lst.pop()