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whisper.py
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whisper.py
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# thanks to https://github.com/openai/whisper for a good chunk of MIT licensed code
import sys
import pathlib
import base64
import multiprocessing
import numpy as np
from typing import Optional
from extra.utils import download_file
from tinygrad.nn.state import torch_load, load_state_dict
from tinygrad.helpers import getenv
import tinygrad.nn as nn
from tinygrad.tensor import Tensor
import itertools
import librosa
# TODO: you have written this fifteen times
class MultiHeadAttention:
def __init__(self, n_state, n_head):
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:Tensor, xa:Optional[Tensor]=None, mask:Optional[Tensor]=None):
q = self.query(x)
k = self.key(xa or x)
v = self.value(xa or x)
wv, qk = self.qkv_attention(q, k, v, mask)
# NOTE: we aren't returning qk
return self.out(wv)
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).permute(0, 2, 1, 3) * scale
k = k.reshape(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
v = v.reshape(*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]
w = qk.softmax(-1)
return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
class ResidualAttentionBlock:
def __init__(self, n_state, n_head, cross_attention=False):
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
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):
x = x + self.attn(self.attn_ln(x), mask=mask)
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
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)
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
class TextDecoder:
def __init__(self, n_vocab, n_text_ctx, n_text_state, n_text_head, n_text_layer, **_):
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, cross_attention=True) for _ in range(n_text_layer)]
self.ln = nn.LayerNorm(n_text_state)
#mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
def __call__(self, x, xa):
offset = 0
x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
seqlen, start_pos = x.shape[1], 0
mask = np.full((1, 1, seqlen, start_pos + seqlen), float("-inf"), dtype=np.float32)
mask = np.triu(mask, k=start_pos + 1) # TODO: this is hard to do in tinygrad
mask = Tensor(mask)
for block in self.blocks: x = block(x, xa, mask)
x = self.ln(x)
return x @ self.token_embedding.weight.T
class Whisper:
def __init__(self, dims):
self.encoder = AudioEncoder(**dims)
self.decoder = TextDecoder(**dims)
def __call__(self, mel:Tensor, tokens:Tensor):
return self.decoder(tokens, self.encoder(mel))
RATE = 16000
CHUNK = 1600
RECORD_SECONDS = 10
def prep_audio(waveform=None, sr=RATE) -> Tensor:
N_FFT = 400
HOP_LENGTH = 160
N_MELS = 80
if waveform is None: waveform = np.zeros(N_FFT, dtype=np.float32)
stft = librosa.stft(waveform, n_fft=N_FFT, hop_length=HOP_LENGTH, window='hann', dtype=np.float32)
magnitudes = stft[..., :-1] ** 2
mel_spec = librosa.filters.mel(sr=sr, n_fft=N_FFT, n_mels=N_MELS) @ magnitudes
log_spec = np.log10(np.clip(mel_spec, 1e-10, mel_spec.max() + 1e8))
log_spec = (log_spec + 4.0) / 4.0
#print(waveform.shape, log_spec.shape)
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",
}
BASE = pathlib.Path(__file__).parents[1] / "weights"
def get_encoding(n_vocab_in):
download_file("https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/gpt2.tiktoken", BASE / "gpt2.tiktoken")
ranks = {base64.b64decode(token): int(rank) for token, rank in (line.split() for line in open(BASE / "gpt2.tiktoken") 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)
assert n_vocab == n_vocab_in
import tiktoken
return tiktoken.Encoding(
name="bob",
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)
def img(x):
import matplotlib.pyplot as plt
plt.imshow(x.numpy())
plt.show()
def listener(q):
prep_audio()
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).reshape(1, -1)
q.put(waveform)
print("done listening")
if __name__ == "__main__":
if getenv("SMALL"):
fn = BASE / "whisper-small.en.pt"
download_file("https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", fn)
else:
fn = BASE / "whisper-tiny.en.pt"
download_file("https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", fn)
state = torch_load(fn)
model = Whisper(state['dims'])
load_state_dict(model, state['model_state_dict'])
enc = get_encoding(state['dims']['n_vocab'])
if len(sys.argv) > 1:
# offline
waveform, sample_rate = librosa.load(sys.argv[1], normalize=True)
log_spec = prep_audio(waveform, sample_rate)
lst = [enc._special_tokens["<|startoftranscript|>"]]
dat = model.encoder(Tensor(log_spec)).realize()
for i in range(50):
out = model.decoder(Tensor([lst]), dat)
out.realize()
idx = out[0,-1].argmax().numpy()
lst.append(idx)
print(enc.decode(lst))
else:
# online
q = multiprocessing.Queue()
p = multiprocessing.Process(target=listener, args=(q,))
p.daemon = True
p.start()
lst = [enc._special_tokens["<|startoftranscript|>"]]
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], axis=1)
did_read = True
if did_read:
last_total = total.shape[1]
log_spec = prep_audio(waveform=Tensor(total).numpy(), sr=RATE)
encoded_audio = model.encoder(Tensor(log_spec)).realize()
out = model.decoder(Tensor([lst]), encoded_audio).realize()
idx = out[0,-1].argmax().numpy()
lst.append(idx)
dec = enc.decode(lst)
print(dec) # DO NOT REMOVE PRINT. IT'S VERY IMPORTANT
if dec.endswith("<|endoftext|>"):
#total = total[:, 320*(len(lst)-1):]
lst = [enc._special_tokens["<|startoftranscript|>"]]