# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Modified from OpenAI Whisper 2022 (https://github.com/openai/whisper/whisper) import os import zlib from dataclasses import dataclass, field from functools import lru_cache from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Union import numpy as np import paddle from ....utils.deps import function_requires_deps, is_dep_available from ...utils.benchmark import ( benchmark, get_inference_operations, set_inference_operations, ) from ..common.tokenizer import GPTTokenizer if is_dep_available("soundfile"): import soundfile if is_dep_available("tqdm"): import tqdm __all__ = [ "Whisper", "Tokenizer", ] def exact_div(x, y): assert x % y == 0 return x // y _MODELS = ["large"] SAMPLE_RATE = 16000 N_FFT = 400 N_MELS = 80 HOP_LENGTH = 160 CHUNK_LENGTH = 30 N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk N_FRAMES = exact_div( N_SAMPLES, HOP_LENGTH ) # 3000: number of frames in a mel spectrogram input @dataclass class ModelDimensions: n_mels: int n_audio_ctx: int n_audio_state: int n_audio_head: int n_audio_layer: int n_vocab: int n_text_ctx: int n_text_state: int n_text_head: int n_text_layer: int 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", "iw": "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", } # language code lookup by name, with a few language aliases TO_LANGUAGE_CODE = { **{language: code for code, language in LANGUAGES.items()}, "burmese": "my", "valencian": "ca", "flemish": "nl", "haitian": "ht", "letzeburgesch": "lb", "pushto": "ps", "panjabi": "pa", "moldavian": "ro", "moldovan": "ro", "sinhalese": "si", "castilian": "es", } def compression_ratio(text) -> float: return len(text) / len(zlib.compress(text.encode("utf-8"))) def format_timestamp( seconds: float, always_include_hours: bool = False, decimal_marker: str = "." ): assert seconds >= 0, "non-negative timestamp expected" milliseconds = round(seconds * 1000.0) hours = milliseconds // 3_600_000 milliseconds -= hours * 3_600_000 minutes = milliseconds // 60_000 milliseconds -= minutes * 60_000 seconds = milliseconds // 1_000 milliseconds -= seconds * 1_000 hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" return ( f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" ) @dataclass(frozen=True) class Tokenizer: """A thin wrapper around `GPTTokenizer` providing quick access to special tokens""" tokenizer: "GPTTokenizer" language: Optional[str] sot_sequence: Tuple[int] def encode(self, text, **kwargs): return self.tokenizer.encode(text, **kwargs) def decode( self, token_ids: Union[int, List[int], np.ndarray, paddle.Tensor], **kwargs ): if len(token_ids) > 1: ids_list = [] for ids in token_ids: if paddle.is_tensor(ids): ids = ids.item() if ids < len(self.tokenizer): ids_list.append(ids) token_ids = ids_list elif len(token_ids) == 1: token_ids = token_ids[0] else: raise ValueError(f"token_ids {token_ids} load error.") return self.tokenizer.decode(token_ids, **kwargs) def decode_with_timestamps(self, tokens) -> str: """ Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". """ outputs = [[]] for token in tokens: if token >= self.timestamp_begin: timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>" outputs.append(timestamp) outputs.append([]) else: outputs[-1].append(token) outputs = [ s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs ] return "".join(outputs) @property @lru_cache() def eot(self) -> int: return self.tokenizer.eos_token_id @property @lru_cache() def sot(self) -> int: return self._get_single_token_id("<|startoftranscript|>") @property @lru_cache() def sot_lm(self) -> int: return self._get_single_token_id("<|startoflm|>") @property @lru_cache() def sot_prev(self) -> int: return self._get_single_token_id("<|startofprev|>") @property @lru_cache() def no_speech(self) -> int: return self._get_single_token_id("<|nospeech|>") @property @lru_cache() def no_timestamps(self) -> int: return self._get_single_token_id("<|notimestamps|>") @property @lru_cache() def timestamp_begin(self) -> int: return self.tokenizer.all_special_ids[-1] + 1 @property @lru_cache() def language_token(self) -> int: """Returns the token id corresponding to the value of the `language` field""" if self.language is None: raise ValueError("This tokenizer does not have language token configured") additional_tokens = dict( zip( self.tokenizer.additional_special_tokens, self.tokenizer.additional_special_tokens_ids, ) ) candidate = f"<|{self.language}|>" if candidate in additional_tokens: return additional_tokens[candidate] raise KeyError(f"Language {self.language} not found in tokenizer.") @property @lru_cache() def all_language_tokens(self) -> Tuple[int]: result = [] for token, token_id in zip( self.tokenizer.additional_special_tokens, self.tokenizer.additional_special_tokens_ids, ): if token.strip("<|>") in LANGUAGES: result.append(token_id) return tuple(result) @property @lru_cache() def all_language_codes(self) -> Tuple[str]: return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) @property @lru_cache() def sot_sequence_including_notimestamps(self) -> Tuple[int]: return tuple(list(self.sot_sequence) + [self.no_timestamps]) @property @lru_cache() def non_speech_tokens(self) -> Tuple[int]: """ Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. - ♪♪♪ - ( SPEAKING FOREIGN LANGUAGE ) - [DAVID] Hey there, keeping basic punctuations like commas, periods, question marks, exclamation points, etc. """ symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』') symbols += ( "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() ) # symbols that may be a single token or multiple tokens depending on the tokenizer. # In case they're multiple tokens, suppress the first token, which is safe because: # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. miscellaneous = set("♩♪♫♬♭♮♯") assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word result = { self.tokenizer.encode(" -").input_ids[0], self.tokenizer.encode(" '").input_ids[0], } for symbol in symbols + list(miscellaneous): for tokens in [ self.tokenizer.encode(symbol).input_ids, self.tokenizer.encode(" " + symbol).input_ids, ]: if len(tokens) == 1 or symbol in miscellaneous: result.add(tokens[0]) return tuple(sorted(result)) def _get_single_token_id(self, text) -> int: tokens = self.tokenizer.encode(text).input_ids assert len(tokens) == 1, f"{text} is not encoded as a single token" return tokens[0] @lru_cache(maxsize=None) def build_tokenizer(resource_path: str, name: str = "gpt2"): os.environ["TOKENIZERS_PARALLELISM"] = "false" path = os.path.join(resource_path, "assets", name) tokenizer = GPTTokenizer.from_pretrained(path) specials = [ "<|startoftranscript|>", *[f"<|{lang}|>" for lang in LANGUAGES.keys()], "<|translate|>", "<|transcribe|>", "<|startoflm|>", "<|startofprev|>", "<|nospeech|>", "<|notimestamps|>", ] tokenizer.add_special_tokens(dict(additional_special_tokens=specials)) return tokenizer @lru_cache(maxsize=None) def get_tokenizer( multilingual: bool, resource_path: str, *, task: Optional[str] = None, # Literal["transcribe", "translate", None] language: Optional[str] = None, ) -> Tokenizer: if language is not None: language = language.lower() if language not in LANGUAGES: if language in TO_LANGUAGE_CODE: language = TO_LANGUAGE_CODE[language] else: raise ValueError(f"Unsupported language: {language}") if multilingual: tokenizer_name = "multilingual" task = task or "transcribe" language = language or "en" else: tokenizer_name = "gpt2" task = None language = None tokenizer = build_tokenizer(resource_path=resource_path, name=tokenizer_name) all_special_ids: List[int] = tokenizer.all_special_ids sot: int = all_special_ids[1] translate: int = all_special_ids[-6] transcribe: int = all_special_ids[-5] langs = tuple(LANGUAGES.keys()) sot_sequence = [sot] if language is not None: sot_sequence.append(sot + 1 + langs.index(language)) if task is not None: sot_sequence.append(transcribe if task == "transcribe" else translate) return Tokenizer( tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence) ) class MultiHeadAttention(paddle.nn.Layer): def __init__(self, n_state: int, n_head: int): super().__init__() self.n_head = n_head self.query = paddle.nn.Linear(n_state, n_state, bias_attr=True) self.key = paddle.nn.Linear(n_state, n_state, bias_attr=False) self.value = paddle.nn.Linear(n_state, n_state, bias_attr=True) self.out = paddle.nn.Linear(n_state, n_state, bias_attr=True) def forward( self, x: paddle.Tensor, xa: Optional[paddle.Tensor] = None, mask: Optional[paddle.Tensor] = None, kv_cache: Optional[dict] = None, ): q = self.query(x) if kv_cache is None or xa is None or self.key not in kv_cache: # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; # otherwise, perform key/value projections for self- or cross-attention as usual. k = self.key(x if xa is None else xa) v = self.value(x if xa is None else xa) else: # for cross-attention, calculate keys and values once and reuse in subsequent calls. k = kv_cache[self.key] v = kv_cache[self.value] wv = self.qkv_attention(q, k, v, mask) return self.out(wv) def qkv_attention( self, q: paddle.Tensor, k: paddle.Tensor, v: paddle.Tensor, mask: Optional[paddle.Tensor] = None, ): n_batch, n_ctx, n_state = q.shape scale = (n_state // self.n_head) ** -0.25 q = ( paddle.transpose(q.reshape([*q.shape[:2], self.n_head, -1]), (0, 2, 1, 3)) * scale ) k = ( paddle.transpose(k.reshape([*k.shape[:2], self.n_head, -1]), (0, 2, 3, 1)) * scale ) v = paddle.transpose(v.reshape([*v.shape[:2], self.n_head, -1]), (0, 2, 1, 3)) qk = q @ k if mask is not None: qk = qk + mask[:n_ctx, :n_ctx] w = paddle.nn.functional.softmax(qk.astype(q.dtype), axis=-1) return paddle.transpose((w @ v), (0, 2, 1, 3)).flatten(start_axis=2) class ResidualAttentionBlock(paddle.nn.Layer): def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): super().__init__() self.attn = MultiHeadAttention(n_state, n_head) self.attn_ln = paddle.nn.LayerNorm(n_state) self.cross_attn = ( MultiHeadAttention(n_state, n_head) if cross_attention else None ) self.cross_attn_ln = paddle.nn.LayerNorm(n_state) if cross_attention else None n_mlp = n_state * 4 self.mlp = paddle.nn.Sequential( paddle.nn.Linear(n_state, n_mlp, bias_attr=True), paddle.nn.GELU(), paddle.nn.Linear(n_mlp, n_state, bias_attr=True), ) self.mlp_ln = paddle.nn.LayerNorm(n_state) def forward( self, x: paddle.Tensor, xa: Optional[paddle.Tensor] = None, mask: Optional[paddle.Tensor] = None, kv_cache: Optional[dict] = None, ): x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache) if self.cross_attn: x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache) x = x + self.mlp(self.mlp_ln(x)) return x def 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 = paddle.exp( -log_timescale_increment * paddle.arange(channels // 2, dtype=paddle.float32) ) scaled_time = ( paddle.arange(length, dtype=paddle.float32)[:, np.newaxis] * inv_timescales[np.newaxis, :] ) return paddle.to_tensor( paddle.concat([paddle.sin(scaled_time), paddle.cos(scaled_time)], axis=1) ) class AudioEncoder(paddle.nn.Layer): def __init__( self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int ): super().__init__() self.conv1 = paddle.nn.Conv1D( n_mels, n_state, kernel_size=3, stride=1, padding=1, bias_attr=True ) self.conv2 = paddle.nn.Conv1D( n_state, n_state, kernel_size=3, stride=2, padding=1, bias_attr=True ) self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) self.blocks: Iterable[ResidualAttentionBlock] = paddle.nn.LayerList( [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] ) self.ln_post = paddle.nn.LayerNorm(n_state) def forward(self, x: paddle.Tensor): """ x : paddle.Tensor, shape = (batch_size, n_mels, n_ctx) the mel spectrogram of the audio """ x = paddle.nn.functional.gelu(self.conv1(x)) x = paddle.nn.functional.gelu(self.conv2(x)) x = paddle.transpose(x, (0, 2, 1)) assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape" x = x + self.positional_embedding for block in self.blocks: x = block(x) x = self.ln_post(x) return x class TextDecoder(paddle.nn.Layer): def __init__( self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int ): super().__init__() self.token_embedding = paddle.nn.Embedding(n_vocab, n_state) self.positional_embedding = paddle.create_parameter( shape=[n_ctx, n_state], dtype="float32" ) self.blocks: Iterable[ResidualAttentionBlock] = paddle.nn.LayerList( [ ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer) ] ) self.ln = paddle.nn.LayerNorm(n_state) mask = paddle.full(shape=[n_ctx, n_state], fill_value=-np.inf, dtype="float32") mask = paddle.triu(mask, diagonal=1) self.register_buffer("mask", mask, persistable=False) def forward( self, x: paddle.Tensor, xa: paddle.Tensor, kv_cache: Optional[dict] = None ): """ x : paddle.LongTensor, shape = (batch_size, <= n_ctx) the text tokens xa : paddle.Tensor, shape = (batch_size, n_mels, n_audio_ctx) the encoded audio features to be attended on """ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 x = ( self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]] ) x = x.to(xa.dtype) for block in self.blocks: x = block(x, xa, mask=self.mask, kv_cache=kv_cache) x = self.ln(x) logits = x @ paddle.transpose(self.token_embedding.weight, (1, 0)) return logits @dataclass(frozen=True) class DecodingOptions: task: str = ( "transcribe" # whether to perform X->X "transcribe" or X->English "translate" ) language: Optional[str] = ( None # language that the audio is in; uses detected language if None ) # sampling-related options temperature: float = 0.0 sample_len: Optional[int] = None # maximum number of tokens to sample best_of: Optional[int] = ( None # number of independent samples to collect, when t > 0 ) beam_size: Optional[int] = None # number of beams in beam search, when t == 0 patience: Optional[float] = ( None # patience in beam search (https://arxiv.org/abs/2204.05424) ) # options for ranking generations (either beams or best-of-N samples) length_penalty: Optional[float] = ( None # "alpha" in Google NMT, None defaults to length norm ) # prompt, prefix, and token suppression prompt: Optional[Union[str, List[int]]] = ( None # text or tokens for the previous context ) prefix: Optional[Union[str, List[int]]] = ( None # text or tokens to prefix the current context ) suppress_blank: bool = True # this will suppress blank outputs # list of tokens ids (or comma-separated token ids) to suppress # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" # timestamp sampling options without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only max_initial_timestamp: Optional[float] = ( 1.0 # the initial timestamp cannot be later than this ) # implementation details fp16: bool = False # use fp16 for most of the calculation @dataclass(frozen=True) class DecodingResult: audio_features: paddle.Tensor language: str language_probs: Optional[Dict[str, float]] = None tokens: List[int] = field(default_factory=list) text: str = "" avg_logprob: float = np.nan no_speech_prob: float = np.nan temperature: float = np.nan compression_ratio: float = np.nan class Inference: def logits( self, tokens: paddle.Tensor, audio_features: paddle.Tensor ) -> paddle.Tensor: """Perform a forward pass on the decoder and return per-token logits""" raise NotImplementedError def rearrange_kv_cache(self, source_indices) -> None: """Update the key-value cache according to the updated beams""" raise NotImplementedError def cleanup_caching(self) -> None: """Clean up any resources or hooks after decoding is finished""" class WhisperInference(Inference): def __init__(self, model: "Whisper", initial_token_length: int): self.model: "Whisper" = model self.initial_token_length = initial_token_length self.kv_cache = {} self.hooks = [] def logits( self, tokens: paddle.Tensor, audio_features: paddle.Tensor ) -> paddle.Tensor: if not self.kv_cache: self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() if tokens.shape[-1] > self.initial_token_length: # only need to use the last token except in the first forward pass tokens = tokens[:, -1:] return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache) def cleanup_caching(self): for hook in self.hooks: hook.remove() self.kv_cache = {} self.hooks = [] def rearrange_kv_cache(self, source_indices): for module, tensor in self.kv_cache.items(): # update the key/value cache to contain the selected sequences self.kv_cache[module] = tensor[source_indices].detach() @paddle.no_grad() def detect_language( model: "Whisper", mel: paddle.Tensor, resource_path: str, tokenizer: Tokenizer = None, ) -> Tuple[paddle.Tensor, List[dict]]: """ Detect the spoken language in the audio, and return them as list of strings, along with the ids of the most probable language tokens and the probability distribution over all language tokens. This is performed outside the main decode loop in order to not interfere with kv-caching. Returns ------- language_tokens : Tensor, shape = (batch_size,) ids of the most probable language tokens, which appears after the startoftranscript token. language_probs : List[Dict[str, float]], length = batch_size list of dictionaries containing the probability distribution over all languages. """ if tokenizer is None: tokenizer = get_tokenizer(model.is_multilingual, resource_path=resource_path) if ( tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence ): raise ValueError( "This model doesn't have language tokens so it can't perform lang id" ) single = mel.ndim == 2 if single: mel = mel.unsqueeze(0) # skip encoder forward pass if already-encoded audio features were given if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): mel = model.encoder(mel) # forward pass using a single token, startoftranscript batch_size = mel.shape[0] x = paddle.to_tensor([[tokenizer.sot]] * batch_size) # [batch_size, 1] logits = model.logits(x, mel)[:, 0] # collect detected languages; suppress all non-language tokens mask = paddle.ones(paddle.to_tensor(logits.shape[-1]), dtype=bool) mask[list(tokenizer.all_language_tokens)] = False logits[:, mask] = -np.inf language_tokens = paddle.argmax(logits, axis=-1) language_token_probs = paddle.nn.functional.softmax(logits, axis=-1) language_probs = [ { c: language_token_probs[i, j].tolist() for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) } for i in range(batch_size) ] if single: language_tokens = language_tokens[0] language_probs = language_probs[0] return language_tokens, language_probs @function_requires_deps("tqdm") def transcribe( model: "Whisper", mel: paddle.Tensor, resource_path: str, *, verbose: Optional[bool] = None, temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), compression_ratio_threshold: Optional[float] = 2.4, logprob_threshold: Optional[float] = -1.0, no_speech_threshold: Optional[float] = 0.6, condition_on_previous_text: bool = True, **decode_options, ): """ Transcribe an audio file using Whisper Parameters ---------- model: Whisper The Whisper model instance mel: paddle.Tensor The audio feature verbose: bool Whether to display the text being decoded to the console. If True, displays all the details, If False, displays minimal details. If None, does not display anything temperature: Union[float, Tuple[float, ...]] Temperature for sampling. It can be a tuple of temperatures, which will be successfully used upon failures according to either `compression_ratio_threshold` or `logprob_threshold`. compression_ratio_threshold: float If the gzip compression ratio is above this value, treat as failed logprob_threshold: float If the average log probability over sampled tokens is below this value, treat as failed no_speech_threshold: float If the no_speech probability is higher than this value AND the average log probability over sampled tokens is below `logprob_threshold`, consider the segment as silent condition_on_previous_text: bool if True, the previous output of the model is provided as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. decode_options: dict Keyword arguments to construct `DecodingOptions` instances Returns ------- A dictionary containing the resulting text ("text") and segment-level details ("segments"), and the spoken language ("language"), which is detected when `decode_options["language"]` is None. """ dtype = np.float32 # paddle only support float32 if dtype == np.float32: decode_options["fp16"] = False if ( decode_options.get("language") == "None" or decode_options.get("language", None) is None ): if not model.is_multilingual: decode_options["language"] = "en" else: if verbose: print( "Detecting language using up to the first 30 seconds. Use `--language` to specify the language" ) segment = pad_or_trim(mel, N_FRAMES) _, probs = model.detect_language(segment, resource_path) decode_options["language"] = max(probs, key=probs.get) if verbose is not None: print( f"Detected language: {LANGUAGES[decode_options['language']].title()}" ) language = decode_options["language"] task = decode_options.get("task", "transcribe") tokenizer = get_tokenizer( model.is_multilingual, resource_path=resource_path, language=language, task=task, ) def decode_with_fallback(segment: paddle.Tensor) -> DecodingResult: temperatures = ( [temperature] if isinstance(temperature, (int, float)) else temperature ) decode_result = None for t in temperatures: kwargs = {**decode_options} if t > 0: # disable beam_size and patience when t > 0 kwargs.pop("beam_size", None) kwargs.pop("patience", None) else: # disable best_of when t == 0 kwargs.pop("best_of", None) options = DecodingOptions(**kwargs, temperature=t) decode_result = model.decode(segment, options, resource_path) needs_fallback = False if ( compression_ratio_threshold is not None and decode_result.compression_ratio > compression_ratio_threshold ): needs_fallback = True # too repetitive if ( logprob_threshold is not None and decode_result.avg_logprob < logprob_threshold ): needs_fallback = True # average log probability is too low if not needs_fallback: break return decode_result seek = 0 input_stride = exact_div( N_FRAMES, model.dims.n_audio_ctx ) # mel frames per output token: 2 time_precision = ( input_stride * HOP_LENGTH / SAMPLE_RATE ) # time per output token: 0.02 (seconds) all_tokens = [] all_segments = [] prompt_reset_since = 0 initial_prompt = decode_options.pop("initial_prompt", None) if initial_prompt and initial_prompt != "None": initial_prompt = tokenizer.encode(" " + initial_prompt.strip()).input_ids all_tokens.extend(initial_prompt) else: initial_prompt = [] def add_segment( *, start: float, end: float, text_tokens: paddle.Tensor, result: DecodingResult, ): text = tokenizer.decode( [token for token in text_tokens if token < tokenizer.eot] ) if len(text.strip()) == 0: # skip empty text output return all_segments.append( { "id": len(all_segments), "seek": seek, "start": start, "end": end, "text": text, "tokens": result.tokens, "temperature": result.temperature, "avg_logprob": result.avg_logprob, "compression_ratio": result.compression_ratio, "no_speech_prob": result.no_speech_prob, } ) if verbose: print(f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}") # show the progress bar when verbose is False (otherwise the transcribed text will be printed) num_frames = mel.shape[-1] previous_seek_value = seek with tqdm.tqdm( total=num_frames, unit="frames", disable=verbose is not False ) as pbar: while seek < num_frames: timestamp_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) segment = pad_or_trim(mel[:, seek:], N_FRAMES) segment_duration = segment.shape[-1] * HOP_LENGTH / SAMPLE_RATE decode_options["prompt"] = all_tokens[prompt_reset_since:] result: DecodingResult = decode_with_fallback(segment) tokens = paddle.to_tensor(result.tokens) if no_speech_threshold is not None: # no voice activity check should_skip = result.no_speech_prob > no_speech_threshold if ( logprob_threshold is not None and result.avg_logprob > logprob_threshold ): # don't skip if the logprob is high enough, despite the no_speech_prob should_skip = False if should_skip: seek += segment.shape[ -1 ] # fast-forward to the next segment boundary continue timestamp_tokens: paddle.Tensor = tokens.greater_equal( paddle.to_tensor(tokenizer.timestamp_begin) ) consecutive = paddle.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] if ( len(consecutive) > 0 ): # if the output contains two consecutive timestamp tokens consecutive = paddle.add(consecutive, paddle.to_tensor(1)) last_slice = 0 for current_slice in consecutive: sliced_tokens = tokens[last_slice:current_slice] start_timestamp_position = ( sliced_tokens[0].item() - tokenizer.timestamp_begin ) end_timestamp_position = ( sliced_tokens[-1].item() - tokenizer.timestamp_begin ) add_segment( start=timestamp_offset + start_timestamp_position * time_precision, end=timestamp_offset + end_timestamp_position * time_precision, text_tokens=sliced_tokens[1:-1], result=result, ) last_slice = current_slice last_timestamp_position = ( tokens[last_slice - 1].item() - tokenizer.timestamp_begin ) seek += last_timestamp_position * input_stride all_tokens.extend(tokens[: last_slice + 1].tolist()) else: duration = segment_duration timestamps = tokens[timestamp_tokens.nonzero().flatten()] if ( len(timestamps) > 0 and timestamps[-1].item() != tokenizer.timestamp_begin ): # no consecutive timestamps but it has a timestamp; use the last one. # single timestamp at the end means no speech after the last timestamp. last_timestamp_position = ( timestamps[-1].item() - tokenizer.timestamp_begin ) duration = last_timestamp_position * time_precision add_segment( start=timestamp_offset, end=timestamp_offset + duration, text_tokens=tokens, result=result, ) seek += segment.shape[-1] all_tokens.extend(tokens.tolist()) if not condition_on_previous_text or result.temperature > 0.5: # do not feed the prompt tokens if a high temperature was used prompt_reset_since = len(all_tokens) # update progress bar pbar.update(min(num_frames, seek) - previous_seek_value) previous_seek_value = seek return dict( text=tokenizer.decode(all_tokens[len(initial_prompt) :]), segments=all_segments, language=language, ) class SequenceRanker: def rank( self, tokens: List[List[paddle.Tensor]], sum_logprobs: List[List[float]] ) -> List[int]: """ Given a list of groups of samples and their cumulative log probabilities, return the indices of the samples in each group to select as the final result """ raise NotImplementedError class MaximumLikelihoodRanker(SequenceRanker): """ Select the sample with the highest log probabilities, penalized using either a simple length normalization or Google NMT paper's length penalty """ def __init__(self, length_penalty: Optional[float]): self.length_penalty = length_penalty def rank(self, tokens: List[List[paddle.Tensor]], sum_logprobs: List[List[float]]): def scores(logprobs, lengths): result = [] for logprob, length in zip(logprobs, lengths): if self.length_penalty is None or self.length_penalty == "None": penalty = length else: # from the Google NMT paper penalty = ((5 + length) / 6) ** self.length_penalty result.append(logprob / penalty) return result # get the sequence with the highest score lengths = [[len(t) for t in s] for s in tokens] return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)] class TokenDecoder: def reset(self): """Initialize any stateful variables for decoding a new sequence""" def update( self, tokens: paddle.Tensor, logits: paddle.Tensor, sum_logprobs: paddle.Tensor, ) -> Tuple[paddle.Tensor, bool]: """Specify how to select the next token, based on the current trace and logits Parameters ---------- tokens : Tensor, shape = (n_batch, current_sequence_length) all tokens in the context so far, including the prefix and sot_sequence tokens logits : Tensor, shape = (n_batch, vocab_size) per-token logits of the probability distribution at the current step sum_logprobs : Tensor, shape = (n_batch) cumulative log probabilities for each sequence Returns ------- tokens : Tensor, shape = (n_batch, current_sequence_length + 1) the tokens, appended with the selected next token completed : bool True if all sequences has reached the end of text """ raise NotImplementedError def finalize( self, tokens: paddle.Tensor, sum_logprobs: paddle.Tensor ) -> Tuple[Sequence[Sequence[paddle.Tensor]], List[List[float]]]: """Finalize search and return the final candidate sequences Parameters ---------- tokens : Tensor, shape = (batch_size, beam_size, current_sequence_length) all tokens in the context so far, including the prefix and sot_sequence sum_logprobs : Tensor, shape = (batch_size, beam_size) cumulative log probabilities for each sequence Returns ------- tokens : Sequence[Sequence[Tensor]], length = batch_size sequence of Tensors containing candidate token sequences, for each audio input sum_logprobs : List[List[float]], length = batch_size sequence of cumulative log probabilities corresponding to the above """ raise NotImplementedError class GreedyDecoder(TokenDecoder): def __init__(self, temperature: float, eot: int): self.temperature = temperature self.eot = eot def update( self, tokens: paddle.Tensor, logits: paddle.Tensor, sum_logprobs: paddle.Tensor, ) -> Tuple[paddle.Tensor, bool]: temperature = self.temperature if temperature == 0: next_tokens = paddle.argmax(logits, axis=-1) else: next_tokens = paddle.distribution.Categorical( logits=logits / temperature ).sample([1]) next_tokens = paddle.reshape( next_tokens, [ next_tokens.shape[0] * next_tokens.shape[1], ], ) logprobs = paddle.nn.functional.log_softmax( logits, axis=-1, dtype=paddle.float32 ) current_logprobs = logprobs[paddle.arange(logprobs.shape[0]), next_tokens] sum_logprobs += current_logprobs * paddle.to_tensor( (tokens[:, -1] != self.eot), dtype=paddle.float32 ) next_tokens[tokens[:, -1] == self.eot] = self.eot tokens = paddle.concat([tokens, next_tokens[:, None]], axis=-1) completed = paddle.all((tokens[:, -1] == self.eot)) return tokens, completed def finalize(self, tokens: paddle.Tensor, sum_logprobs: paddle.Tensor): # make sure each sequence has at least one EOT token at the end tokens = paddle.nn.functional.pad( tokens, (0, 1), value=self.eot, data_format="NCL" ) return tokens, sum_logprobs.tolist() class BeamSearchDecoder(TokenDecoder): def __init__( self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None, ): self.beam_size = beam_size self.eot = eot self.inference = inference self.patience = patience or 1.0 if patience is None or patience == "None": self.patience = 1.0 else: self.patience = patience self.max_candidates: int = round(beam_size * self.patience) self.finished_sequences = None assert ( self.max_candidates > 0 ), f"Invalid beam size ({beam_size}) or patience ({patience})" def reset(self): self.finished_sequences = None def update( self, tokens: paddle.Tensor, logits: paddle.Tensor, sum_logprobs: paddle.Tensor, ) -> Tuple[paddle.Tensor, bool]: if tokens.shape[0] % self.beam_size != 0: raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") batch_size = tokens.shape[0] // self.beam_size if self.finished_sequences is None: # for the first update self.finished_sequences = [{} for _ in range(batch_size)] logprobs = paddle.nn.functional.log_softmax(logits, axis=-1, dtype="float32") next_tokens, source_indices, finished_sequences = [], [], [] for i in range(batch_size): scores, sources, finished = {}, {}, {} # STEP 1: calculate the cumulative log probabilities for possible candidates for j in range(self.beam_size): idx = i * self.beam_size + j prefix = tokens[idx].tolist() logprob, token = paddle.topk(logprobs[idx], k=self.beam_size + 1) for logprob, token in zip(logprob, token): new_logprob = (sum_logprobs[idx] + logprob).item() sequence = tuple(prefix + [token.item()]) scores[sequence] = new_logprob sources[sequence] = idx # STEP 2: rank the candidates and keep the top beam_size sequences for each audio saved = 0 for sequence in sorted(scores, key=scores.get, reverse=True): if sequence[-1] == self.eot: finished[sequence] = scores[sequence] else: sum_logprobs[len(next_tokens)] = scores[sequence] next_tokens.append(sequence) source_indices.append(sources[sequence]) saved += 1 if saved == self.beam_size: break finished_sequences.append(finished) tokens = paddle.to_tensor(next_tokens) self.inference.rearrange_kv_cache(source_indices) # add newly finished sequences to self.finished_sequences assert len(self.finished_sequences) == len(finished_sequences) for previously_finished, newly_finished in zip( self.finished_sequences, finished_sequences ): for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): if len(previously_finished) >= self.max_candidates: break # the candidate list is full previously_finished[seq] = newly_finished[seq] # mark as completed if all audio has enough number of samples completed = all( len(sequences) >= self.max_candidates for sequences in self.finished_sequences ) return tokens, completed def finalize(self, preceding_tokens: paddle.Tensor, sum_logprobs: paddle.Tensor): # collect all finished sequences, including patience, and add unfinished ones if not enough sum_logprobs = sum_logprobs.cpu() for i, sequences in enumerate(self.finished_sequences): if ( len(sequences) < self.beam_size ): # when not enough sequences are finished for j in list(np.argsort(sum_logprobs[i]))[::-1]: sequence = preceding_tokens[i, j].tolist() + [self.eot] sequences[tuple(sequence)] = sum_logprobs[i][j].item() if len(sequences) >= self.beam_size: break tokens: List[List[paddle.Tensor]] = [ [paddle.to_tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences ] sum_logprobs: List[List[float]] = [ list(sequences.values()) for sequences in self.finished_sequences ] return tokens, sum_logprobs class LogitFilter: def apply(self, logits: paddle.Tensor, tokens: paddle.Tensor) -> None: """Apply any filtering or masking to logits in-place Parameters ---------- logits : Tensor, shape = (n_batch, vocab_size) per-token logits of the probability distribution at the current step tokens : Tensor, shape = (n_batch, current_sequence_length) all tokens in the context so far, including the prefix and sot_sequence tokens """ raise NotImplementedError class SuppressBlank(LogitFilter): def __init__(self, tokenizer: Tokenizer, sample_begin: int): self.tokenizer = tokenizer self.sample_begin = sample_begin def apply(self, logits: paddle.Tensor, tokens: paddle.Tensor): if tokens.shape[1] == self.sample_begin: logits.contiguous() logits[:, self.tokenizer.encode(" ").input_ids + [self.tokenizer.eot]] = ( -np.inf ) class SuppressTokens(LogitFilter): def __init__(self, suppress_tokens: Sequence[int]): self.suppress_tokens = list(suppress_tokens) def apply(self, logits: paddle.Tensor, tokens: paddle.Tensor): logits.contiguous() logits[:, self.suppress_tokens] = -np.inf class ApplyTimestampRules(LogitFilter): def __init__( self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int], ): self.tokenizer = tokenizer self.sample_begin = sample_begin self.max_initial_timestamp_index = max_initial_timestamp_index def apply(self, logits: paddle.Tensor, tokens: paddle.Tensor): # suppress <|notimestamps|> which is handled by without_timestamps if self.tokenizer.no_timestamps is not None: logits.contiguous() logits[:, self.tokenizer.no_timestamps] = -np.inf # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly for k in range(tokens.shape[0]): seq = [t for t in tokens[k, self.sample_begin :].tolist()] last_was_timestamp = ( len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin ) penultimate_was_timestamp = ( len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin ) if last_was_timestamp: logits.contiguous() if penultimate_was_timestamp: # has to be non-timestamp logits[k, self.tokenizer.timestamp_begin :] = -np.inf else: # cannot be normal text tokens logits[k, : self.tokenizer.eot] = -np.inf # apply the `max_initial_timestamp` option if ( tokens.shape[1] == self.sample_begin and self.max_initial_timestamp_index is not None ): last_allowed = ( self.tokenizer.timestamp_begin + self.max_initial_timestamp_index ) logits.contiguous() logits[:, last_allowed + 1 :] = -np.inf # if sum of probability over timestamps is above any other token, sample timestamp logprobs = paddle.nn.functional.log_softmax(logits, axis=-1, dtype="float32") for k in range(tokens.shape[0]): # When using paddle.logsumexp on a 32GB Tesla-V100 GPU, we encountered CUDA error 700. # To bypass this issue in CI, we have decomposed the operation into separate steps. # It will raise 2e-6 difference in precision. # TODO: revert this after logsumexp been fixed. timestamp_logprob = paddle.exp( logprobs[k, self.tokenizer.timestamp_begin :] ) timestamp_logprob = paddle.sum(timestamp_logprob, axis=-1) timestamp_logprob = paddle.log(timestamp_logprob) max_text_token_logprob = paddle.max( logprobs[k, : self.tokenizer.timestamp_begin] ) if timestamp_logprob > max_text_token_logprob: logits.contiguous() logits[k, : self.tokenizer.timestamp_begin] = -np.inf class DecodingTask: inference: Inference sequence_ranker: SequenceRanker decoder: TokenDecoder logit_filters: List[LogitFilter] def __init__(self, model: "Whisper", options: DecodingOptions, resource_path: str): self.model = model language = options.language or "en" tokenizer = get_tokenizer( model.is_multilingual, resource_path=resource_path, language=language, task=options.task, ) self.tokenizer: Tokenizer = tokenizer self.options: DecodingOptions = self._verify_options(options) self.resource_path: str = resource_path self.beam_size: int = options.beam_size or options.best_of or 1 self.n_ctx: int = model.dims.n_text_ctx self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2 self.sot_sequence: Tuple[int] = tokenizer.sot_sequence if self.options.without_timestamps: self.sot_sequence = tokenizer.sot_sequence_including_notimestamps self.initial_tokens: Tuple[int] = self._get_initial_tokens() self.sample_begin: int = len(self.initial_tokens) self.sot_index: int = self.initial_tokens.index(tokenizer.sot) # inference: implements the forward pass through the decoder, including kv caching self.inference = WhisperInference(model, len(self.initial_tokens)) # sequence ranker: implements how to rank a group of sampled sequences self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty) # decoder: implements how to select the next tokens, given the autoregressive distribution if options.beam_size is not None: self.decoder = BeamSearchDecoder( options.beam_size, tokenizer.eot, self.inference, options.patience ) else: self.decoder = GreedyDecoder(options.temperature, tokenizer.eot) # logit filters: applies various rules to suppress or penalize certain tokens self.logit_filters = [] if self.options.suppress_blank: self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) if self.options.suppress_tokens: self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) if not options.without_timestamps: precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds max_initial_timestamp_index = None if options.max_initial_timestamp: max_initial_timestamp_index = round( self.options.max_initial_timestamp / precision ) self.logit_filters.append( ApplyTimestampRules( tokenizer, self.sample_begin, max_initial_timestamp_index ) ) def _verify_options(self, options: DecodingOptions) -> DecodingOptions: if options.beam_size is not None and options.best_of is not None: raise ValueError("beam_size and best_of can't be given together") if options.temperature == 0: if options.best_of is not None: raise ValueError("best_of with greedy sampling (T=0) is not compatible") if options.patience is not None and options.beam_size is None: raise ValueError("patience requires beam_size to be given") if options.length_penalty is not None and options.length_penalty != "None": if not (0 <= options.length_penalty <= 1): raise ValueError( "length_penalty (alpha) should be a value between 0 and 1" ) return options def _get_initial_tokens(self) -> Tuple[int]: tokens = list(self.sot_sequence) prefix = self.options.prefix prompt = self.options.prompt if prefix: prefix_tokens = ( self.tokenizer.encode(" " + prefix.strip().input_ids) if isinstance(prefix, str) else prefix ) if self.sample_len is not None: max_prefix_len = self.n_ctx // 2 - self.sample_len prefix_tokens = prefix_tokens[-max_prefix_len:] tokens = tokens + prefix_tokens if prompt: prompt_tokens = ( self.tokenizer.encode(" " + prompt.strip().input_ids) if isinstance(prompt, str) else prompt ) tokens = ( [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens ) return tuple(tokens) def _get_suppress_tokens(self) -> Tuple[int]: suppress_tokens = self.options.suppress_tokens if isinstance(suppress_tokens, str): suppress_tokens = [int(t) for t in suppress_tokens.split(",")] if -1 in suppress_tokens: suppress_tokens = [t for t in suppress_tokens if t >= 0] suppress_tokens.extend(self.tokenizer.non_speech_tokens) elif suppress_tokens is None or len(suppress_tokens) == 0: suppress_tokens = [] # interpret empty string as an empty list else: assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" suppress_tokens.extend( [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] ) if self.tokenizer.no_speech is not None: # no-speech probability is collected separately suppress_tokens.append(self.tokenizer.no_speech) return tuple(sorted(set(suppress_tokens))) def _get_audio_features(self, mel: paddle.Tensor): if mel.shape[-2:] == ( self.model.dims.n_audio_ctx, self.model.dims.n_audio_state, ): # encoded audio features are given; skip audio encoding audio_features = mel else: audio_features = self.model.encoder(mel) return audio_features def _detect_language( self, audio_features: paddle.Tensor, tokens: paddle.Tensor, resource_path: str, ): languages = [self.options.language] * audio_features.shape[0] lang_probs = None if self.options.language is None or self.options.task == "lang_id": lang_tokens, lang_probs = self.model.detect_language( audio_features, self.tokenizer, self.resource_path ) languages = [max(probs, key=probs.get) for probs in lang_probs] if self.options.language is None: tokens[:, self.sot_index + 1] = lang_tokens # write language tokens return languages, lang_probs def _main_loop(self, audio_features: paddle.Tensor, tokens: paddle.Tensor): assert audio_features.shape[0] == tokens.shape[0] n_batch = tokens.shape[0] sum_logprobs: paddle.Tensor = paddle.zeros( paddle.to_tensor(n_batch), dtype=paddle.float32 ) no_speech_probs = [np.nan] * n_batch try: for i in range(self.sample_len): logits = self.inference.logits(tokens, audio_features) if ( i == 0 and self.tokenizer.no_speech is not None ): # save no_speech_probs probs_at_sot = paddle.nn.functional.softmax( logits[:, self.sot_index], axis=-1, dtype=paddle.float32 ) no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() # now we need to consider the logits at the last token only logits = logits[:, -1] # apply the logit filters, e.g. for suppressing or applying penalty to for logit_filter in self.logit_filters: logit_filter.apply(logits, tokens) # expand the tokens tensor with the selected next tokens tokens, completed = self.decoder.update(tokens, logits, sum_logprobs) if completed or tokens.shape[-1] > self.n_ctx: break finally: self.inference.cleanup_caching() return tokens, sum_logprobs, no_speech_probs @paddle.no_grad() def run(self, mel: paddle.Tensor) -> List[DecodingResult]: self.decoder.reset() tokenizer: Tokenizer = self.tokenizer batch_size: int = mel.shape[0] audio_features: paddle.Tensor = self._get_audio_features( mel ) # encoder forward pass tokens: paddle.Tensor if batch_size > 1: for i in range(batch_size): tokens = paddle.concat( x=[ paddle.to_tensor([self.initial_tokens]), paddle.to_tensor([self.initial_tokens]), ], axis=0, ) elif batch_size == 1: tokens = paddle.to_tensor([self.initial_tokens]) # detect language if requested, overwriting the language token languages, language_probs = self._detect_language( paddle.to_tensor(audio_features), paddle.to_tensor(tokens), self.resource_path, ) if self.options.task == "lang_id": return [ DecodingResult( audio_features=features, language=language, language_probs=probs ) for features, language, probs in zip( audio_features, languages, language_probs ) ] # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling audio_features = paddle.repeat_interleave( audio_features, self.beam_size, axis=0 ) tokens = paddle.repeat_interleave(tokens, self.beam_size, axis=0) # call the main sampling loop tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens) # reshape the tensors to have (batch_size, beam_size) as the first two dimensions audio_features = audio_features[:: self.beam_size] no_speech_probs = no_speech_probs[:: self.beam_size] assert audio_features.shape[0] == len(no_speech_probs) == batch_size tokens = tokens.reshape([batch_size, self.beam_size, -1]) sum_logprobs = sum_logprobs.reshape([batch_size, self.beam_size]) # get the final candidates for each group, and slice between the first sampled token and EOT tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) tokens: List[List[paddle.Tensor]] = [ [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens ] # select the top-ranked sample in each group selected = self.sequence_ranker.rank(tokens, sum_logprobs) tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] avg_logprobs: List[float] = [ lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs) ] fields = ( texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs, ) if len(set(map(len, fields))) != 1: raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") return [ DecodingResult( audio_features=features, language=language, tokens=tokens, text=text, avg_logprob=avg_logprob, no_speech_prob=no_speech_prob, temperature=self.options.temperature, compression_ratio=compression_ratio(text), ) for text, language, tokens, features, avg_logprob, no_speech_prob in zip( *fields ) ] @paddle.no_grad() def decode( model: "Whisper", mel: paddle.Tensor, options: DecodingOptions = DecodingOptions(), resource_path=str, ) -> Union[DecodingResult, List[DecodingResult]]: """ Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). Parameters ---------- model: Whisper the Whisper model instance mel: paddle.Tensor, shape = (80, 3000) or (*, 80, 3000) A tensor containing the Mel spectrogram(s) options: DecodingOptions A dataclass that contains all necessary options for decoding 30-second segments Returns ------- result: Union[DecodingResult, List[DecodingResult]] The result(s) of decoding contained in `DecodingResult` dataclass instance(s) """ single = mel.ndim == 2 if single: mel = mel.unsqueeze(0) result = DecodingTask(model, options, resource_path).run(mel) if single: result = result[0] return result class Whisper(paddle.nn.Layer): """ The `Whisper` module use AudioEncoder and TextDecoder, and return detect_language, transcribe, decode. """ def __init__(self, dims: ModelDimensions): super().__init__() self.dims = dims self.encoder = AudioEncoder( self.dims.n_mels, self.dims.n_audio_ctx, self.dims.n_audio_state, self.dims.n_audio_head, self.dims.n_audio_layer, ) self.decoder = TextDecoder( self.dims.n_vocab, self.dims.n_text_ctx, self.dims.n_text_state, self.dims.n_text_head, self.dims.n_text_layer, ) def embed_audio(self, mel: paddle.Tensor): return self.encoder.forward(mel) def logits(self, tokens: paddle.Tensor, audio_features: paddle.Tensor): return self.decoder.forward(tokens, audio_features) def forward( self, mel: paddle.Tensor, tokens: paddle.Tensor ) -> Dict[str, paddle.Tensor]: return self.decoder(tokens, self.encoder(mel)) @property def device(self): return paddle.device.get_device() @property def is_multilingual(self): return self.dims.n_vocab == 51865 def install_kv_cache_hooks(self, cache: Optional[dict] = None): """ The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value tensors calculated for the previous positions. This method returns a dictionary that stores all caches, and the necessary hooks for the key and value projection modules that save the intermediate tensors to be reused during later calculations. Returns ------- cache : Dict[nn.Layer, paddle.Tensor] A dictionary object mapping the key/value projection modules to its cache hooks : List[RemovableHandle] List of PyTorch RemovableHandle objects to stop the hooks to be called """ cache = {**cache} if cache is not None else {} hooks = [] def save_to_cache(module, _, output): if ( module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0] ): cache[module] = ( output # save as-is, for the first token or cross attention ) else: cache[module] = paddle.concat([cache[module], output], axis=1).detach() return cache[module] def install_hooks(layer: paddle.nn.Layer): if isinstance(layer, MultiHeadAttention): hooks.append(layer.key.register_forward_post_hook(save_to_cache)) hooks.append(layer.value.register_forward_post_hook(save_to_cache)) self.decoder.apply(install_hooks) return cache, hooks detect_language = detect_language set_inference_operations(get_inference_operations() + ["speech_transcribe"]) transcribe = benchmark.timeit_with_options(name="speech_transcribe")(transcribe) decode = decode def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1): """ Pad or trim the audio array to N_SAMPLES, as expected by the encoder. """ if paddle.is_tensor(array): if array.shape[axis] > length: array = array.index_select(axis=axis, index=paddle.arange(length)) if array.shape[axis] < length: pad_widths = [(0, 0)] * array.ndim pad_widths[axis] = (0, length - array.shape[axis]) array = paddle.transpose(array, (1, 0)) array = paddle.nn.functional.pad( array, [pad for sizes in pad_widths[::-1] for pad in sizes], data_format="NLC", ) array = paddle.transpose(array, (1, 0)) else: if array.shape[axis] > length: array = array.take(indices=range(length), axis=axis) if array.shape[axis] < length: pad_widths = [(0, 0)] * array.ndim pad_widths[axis] = (0, length - array.shape[axis]) array = paddle.transpose(array, (1, 0)) array = np.pad(array, pad_widths) array = paddle.transpose(array, (1, 0)) return array def hann_window(n_fft: int = N_FFT): """ hanning window n_fft: The number of frequency components of the discrete Fourier transform. """ return paddle.to_tensor( [0.5 - 0.5 * np.cos(2 * np.pi * n / n_fft) for n in range(n_fft)], dtype=paddle.float32, ) @lru_cache(maxsize=None) def mel_filters(resource_path: str, n_mels: int = N_MELS) -> paddle.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), ) """ assert n_mels == 80, f"Unsupported n_mels: {n_mels}" with np.load(os.path.join(resource_path, "assets", "mel_filters.npz")) as f: return paddle.to_tensor(f[f"mel_{n_mels}"]) @function_requires_deps("soundfile") def log_mel_spectrogram( audio: Union[str, np.ndarray, paddle.Tensor], n_mels: int = N_MELS, resource_path: str = None, ): """ Compute the log-Mel spectrogram of Parameters ---------- audio: Union[str, np.ndarray, paddle.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 Returns ------- paddle.Tensor, shape = (80, n_frames) A Tensor that contains the Mel spectrogram """ if not paddle.is_tensor(audio): if isinstance(audio, str): audio, _ = soundfile.read(audio, dtype="float32", always_2d=True) audio = audio[:, 0] audio = paddle.to_tensor(audio) window = hann_window(N_FFT) stft = paddle.signal.stft(audio, N_FFT, HOP_LENGTH, window=window) magnitudes = stft[:, :-1].abs() ** 2 filters = mel_filters(resource_path, n_mels) mel_spec = filters @ magnitudes mel_spec = paddle.to_tensor(mel_spec.numpy().tolist()) log_spec = paddle.clip(mel_spec, min=1e-10).log10() log_spec = paddle.maximum(log_spec, log_spec.max() - 8.0) log_spec = (log_spec + 4.0) / 4.0 return log_spec