exo/examples/whisper.py

# thanks to https://github.com/openai/whisper for a good chunk of MIT licensed code

import sys, base64, multiprocessing, itertools
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.blocks_start_tok = [TinyJit(block.__call__) for block in self.blocks]
    self.blocks_after_start_tok = [TinyJit(block.__call__) for block in self.blocks]
    self.start_output_tok = TinyJit(self.output_tok)
    self.after_start_output_tok = TinyJit(self.output_tok)

  # if layernorm supported symbolic shapes, we wouldn't need this hacky 'streaming' param (which should be called something more descriptive like 'x_is_start_toks_only')
  def __call__(self, x: Tensor, pos: int, encoded_audio: Tensor, streaming=False):
    seqlen = x.shape[-1]
    x = self.token_embedding(x) + self.positional_embedding[pos:pos+seqlen]
    if pos == 0:
      for block in (self.blocks if streaming else self.blocks_start_tok):
        x = block(x, xa=encoded_audio, mask=self.mask, len=0)  # pass xa for cross attn kv caching
      return self.output_tok(x) if streaming else self.start_output_tok(x)
    else:
      for block in self.blocks_after_start_tok:
        len_v = Variable("self_attn_cache_len", 1, self.max_self_attn_cache_len).bind(pos)
        x = block(x, mask=self.mask, len=len_v)
      return self.after_start_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() - 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, 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
  """
  N_audio = len(waveforms)
  log_spec = prep_audio(waveforms, model.batch_size, truncate)

  if log_spec.shape[-1] > FRAMES_PER_SEGMENT and N_audio > 1:
    # we don't support multi-segment batching because the size of the prompt tokens would be different for each item in the batch
    # if we really want this feature, we can consider padding or trimming prompt tokens of varying lengths to make them consistent
    raise Exception("Multi-segment transcription not supported with batch audio input")

  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|>"])
  transcription_start_index = len(start_tokens)
  eot = enc._special_tokens["<|endoftext|>"]
  transcription_tokens = [np.array([], dtype=np.int32)] * log_spec.shape[0]

  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]))
    pos = 0
    curr_segment_tokens = np.tile(start_tokens, (log_spec.shape[0], 1))
    if curr_frame > 0:
      # pass the previously inferred tokens as 'prompt' - https://github.com/openai/whisper/discussions/117#discussioncomment-3727051
      prompt = np.concatenate((
        [enc._special_tokens["<|startofprev|>"]],
        transcription_tokens[0][-model.decoder.max_tokens_to_sample+1:],
        start_tokens))
      curr_segment_tokens = np.tile(prompt, (log_spec.shape[0], 1))
      transcription_start_index = len(curr_segment_tokens[0])

    for i in range(model.decoder.max_tokens_to_sample):
      out = model.decoder(Tensor(curr_segment_tokens if i == 0 else curr_segment_tokens[:, -1:]), pos, encoded_audio, streaming=curr_frame > 0)
      next_tokens = out[:, -1].argmax(axis=-1).numpy().astype(np.int32)
      next_tokens[curr_segment_tokens[:, -1] == eot] = eot
      curr_segment_tokens = np.concatenate((curr_segment_tokens, next_tokens.reshape(-1, 1)), axis=1)
      pos = curr_segment_tokens.shape[-1] - 1
      if DEBUG >= 1: print(i, list(map(lambda tokens: enc.decode(tokens), curr_segment_tokens)))
      if (curr_segment_tokens[:, -1] == eot).all():
        break

    for i, t in enumerate(curr_segment_tokens):
      eot_index = np.where(t == eot)[0]
      eot_index = None if len(eot_index) == 0 else eot_index[0]
      transcription_tokens[i] = np.concatenate((transcription_tokens[i], t[transcription_start_index:eot_index]))

  transcriptions = list(map(lambda tokens: enc.decode(tokens).strip(), transcription_tokens))
  return transcriptions[:N_audio] if N_audio > 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()