from dataclasses import dataclass, field from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING import numpy as np import torch import torch.nn.functional as F from torch import Tensor from torch.distributions import Categorical from .audio import CHUNK_LENGTH from .tokenizer import Tokenizer, get_tokenizer from .utils import compression_ratio if TYPE_CHECKING: from .model import Whisper @torch.no_grad() def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[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 = (n_audio,) ids of the most probable language tokens, which appears after the startoftranscript token. language_probs : List[Dict[str, float]], length = n_audio list of dictionaries containing the probability distribution over all languages. """ if tokenizer is None: tokenizer = get_tokenizer(model.is_multilingual) if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence: raise ValueError(f"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 n_audio = mel.shape[0] x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1] logits = model.logits(x, mel)[:, 0] # collect detected languages; suppress all non-language tokens mask = torch.ones(logits.shape[-1], dtype=torch.bool) mask[list(tokenizer.all_language_tokens)] = False logits[:, mask] = -np.inf language_tokens = logits.argmax(dim=-1) language_token_probs = logits.softmax(dim=-1).cpu() language_probs = [ { c: language_token_probs[i, j].item() for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) } for i in range(n_audio) ] if single: language_tokens = language_tokens[0] language_probs = language_probs[0] return language_tokens, language_probs @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 = True # use fp16 for most of the calculation @dataclass(frozen=True) class DecodingResult: audio_features: 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: Tensor, audio_features: Tensor) -> 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""" pass class PyTorchInference(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: Tensor, audio_features: Tensor) -> 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() class SequenceRanker: def rank(self, tokens: List[List[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[Tensor]], sum_logprobs: List[List[float]]): def scores(logprobs, lengths): result = [] for logprob, length in zip(logprobs, lengths): if self.length_penalty is 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: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[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: Tensor, sum_logprobs: Tensor ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]: """Finalize search and return the final candidate sequences Parameters ---------- tokens : Tensor, shape = (n_audio, n_group, current_sequence_length) all tokens in the context so far, including the prefix and sot_sequence sum_logprobs : Tensor, shape = (n_audio, n_group) cumulative log probabilities for each sequence Returns ------- tokens : Sequence[Sequence[Tensor]], length = n_audio sequence of Tensors containing candidate token sequences, for each audio input sum_logprobs : List[List[float]], length = n_audio 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: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: temperature = self.temperature if temperature == 0: next_tokens = logits.argmax(dim=-1) else: next_tokens = Categorical(logits=logits / temperature).sample() logprobs = F.log_softmax(logits.float(), dim=-1) current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot) next_tokens[tokens[:, -1] == self.eot] = self.eot tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1) completed = (tokens[:, -1] == self.eot).all() return tokens, completed def finalize(self, tokens: Tensor, sum_logprobs: Tensor): # make sure each sequence has at least one EOT token at the end tokens = F.pad(tokens, (0, 1), value=self.eot) 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 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: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: if tokens.shape[0] % self.beam_size != 0: raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") n_audio = tokens.shape[0] // self.beam_size if self.finished_sequences is None: # for the first update self.finished_sequences = [{} for _ in range(n_audio)] logprobs = F.log_softmax(logits.float(), dim=-1) next_tokens, source_indices, finished_sequences = [], [], [] for i in range(n_audio): 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() for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): 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 = torch.tensor(next_tokens, device=tokens.device) 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: Tensor, sum_logprobs: 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 # print(f'self.finished_sequences = {self.finished_sequences}') tokens: List[List[Tensor]] = [ [torch.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: Tensor, tokens: 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: Tensor, tokens: Tensor): if tokens.shape[1] == self.sample_begin: logits[:, self.tokenizer.encode(" ") + [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: Tensor, tokens: Tensor): 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: Tensor, tokens: Tensor): # suppress <|notimestamps|> which is handled by without_timestamps if self.tokenizer.no_timestamps is not None: 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: 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[:, last_allowed + 1 :] = -np.inf # if sum of probability over timestamps is above any other token, sample timestamp logprobs = F.log_softmax(logits.float(), dim=-1) for k in range(tokens.shape[0]): timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1) max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() if timestamp_logprob > max_text_token_logprob: 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): self.model = model language = options.language or "en" tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task) self.tokenizer: Tokenizer = tokenizer self.options: DecodingOptions = self._verify_options(options) self.n_group: 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 = PyTorchInference(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 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()) 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()) 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: Tensor): if self.options.fp16: mel = mel.half() 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) if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32): return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}") return audio_features def _detect_language(self, audio_features: Tensor, tokens: Tensor): 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) 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: Tensor, tokens: Tensor): assert audio_features.shape[0] == tokens.shape[0] n_batch = tokens.shape[0] sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) 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 = logits[:, self.sot_index].float().softmax(dim=-1) 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 @torch.no_grad() def run(self, mel: Tensor) -> List[DecodingResult]: self.decoder.reset() tokenizer: Tokenizer = self.tokenizer n_audio: int = mel.shape[0] audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) # detect language if requested, overwriting the language token languages, language_probs = self._detect_language(audio_features, tokens) 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 text tensors by the group size, for beam search or best-of-n sampling audio_features = audio_features.repeat_interleave(self.n_group, dim=0) tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) # call the main sampling loop # print(audio_features.shape, tokens.shape) # torch.Size([1, 1500, 1280]) torch.Size([50, 3]) tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens) # reshape the tensors to have (n_audio, n_group) as the first two dimensions audio_features = audio_features[:: self.n_group] no_speech_probs = no_speech_probs[:: self.n_group] assert audio_features.shape[0] == len(no_speech_probs) == n_audio tokens = tokens.reshape(n_audio, self.n_group, -1) sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) # 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[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 ) ] @torch.no_grad() def run1(self, mel: Tensor) -> List[DecodingResult]: self.decoder.reset() tokenizer: Tokenizer = self.tokenizer n_audio: int = mel.shape[0] audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) # detect language if requested, overwriting the language token languages, language_probs = self._detect_language(audio_features, tokens) 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 = audio_features.repeat_interleave(self.n_group, dim=0) tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) # call the main sampling loop tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens) # reshape the tensors to have (n_audio, n_group) as the first two dimensions audio_features = audio_features[:: self.n_group] no_speech_probs = no_speech_probs[:: self.n_group] assert audio_features.shape[0] == len(no_speech_probs) == n_audio tokens = tokens.reshape(n_audio, self.n_group, -1) sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) # 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[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) # print(f'selected = {selected}\n') tokens = [[i.tolist() for i in t] for t in tokens] avg_logprobs: List[float] = [[ilp / (len(it) + 1) for it, ilp in zip(t, lp)] for t, lp in zip(tokens, sum_logprobs)] texts = [[tokenizer.decode(i).strip() for i in t] for t in tokens] # print(f'tokens = {tokens}') # print(f'avg_logprobs = {avg_logprobs}') # print(f'texts = {texts}') import heapq top50_list = [heapq.nlargest(50, range(len(a)), a.__getitem__) for a in avg_logprobs] # print(f'top50_list = {top50_list}') texts = [[text[i] for i in top50] for text, top50 in zip(texts, top50_list)] return texts @torch.no_grad() def run_wer(self, mel: Tensor) -> List[DecodingResult]: self.decoder.reset() tokenizer: Tokenizer = self.tokenizer n_audio: int = mel.shape[0] audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) # detect language if requested, overwriting the language token languages, language_probs = self._detect_language(audio_features, tokens) 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 = audio_features.repeat_interleave(self.n_group, dim=0) tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) # call the main sampling loop tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens) # reshape the tensors to have (n_audio, n_group) as the first two dimensions audio_features = audio_features[:: self.n_group] no_speech_probs = no_speech_probs[:: self.n_group] assert audio_features.shape[0] == len(no_speech_probs) == n_audio tokens = tokens.reshape(n_audio, self.n_group, -1) sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) # 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[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) # print(f'selected = {selected}\n') tokens = [[i.tolist() for i in t] for t in tokens] avg_logprobs: List[float] = [[ilp / (len(it) + 1) for it, ilp in zip(t, lp)] for t, lp in zip(tokens, sum_logprobs)] texts = [[tokenizer.decode(i).strip() for i in t] for t in tokens] # print(f'tokens = {tokens}') # print(f'avg_logprobs = {avg_logprobs}') # print(f'texts = {texts}') import heapq top5_list = [heapq.nlargest(5, range(len(a)), a.__getitem__) for a in avg_logprobs] # print(f'top5_list = {top5_list}') texts = [[text[i] for i in top5] for text, top5 in zip(texts, top5_list)] return texts @torch.no_grad() def run_score(self, mel: Tensor) -> List[DecodingResult]: self.decoder.reset() tokenizer: Tokenizer = self.tokenizer n_audio: int = mel.shape[0] audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) # detect language if requested, overwriting the language token languages, language_probs = self._detect_language(audio_features, tokens) 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 = audio_features.repeat_interleave(self.n_group, dim=0) tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) # call the main sampling loop tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens) # reshape the tensors to have (n_audio, n_group) as the first two dimensions audio_features = audio_features[:: self.n_group] no_speech_probs = no_speech_probs[:: self.n_group] assert audio_features.shape[0] == len(no_speech_probs) == n_audio tokens = tokens.reshape(n_audio, self.n_group, -1) sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) # 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[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) # print(f'selected = {selected}\n') tokens = [[i.tolist() for i in t] for t in tokens] avg_logprobs: List[float] = [[ilp / (len(it) + 1) for it, ilp in zip(t, lp)] for t, lp in zip(tokens, sum_logprobs)] texts = [[tokenizer.decode(i).strip() for i in t] for t in tokens] # print(f'tokens = {tokens}') # print(f'avg_logprobs = {avg_logprobs}') # print(f'texts = {texts}') import heapq top50_list = [heapq.nlargest(80, range(len(a)), a.__getitem__) for a in avg_logprobs] # print(f'top50_list = {top50_list}') texts = [[text[i] for i in top50] for text, top50 in zip(texts, top50_list)] scores = [[score[i] for i in top50] for score, top50 in zip(avg_logprobs, top50_list)] return texts, scores @torch.no_grad() def decode( model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions(), **kwargs, ) -> 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: torch.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) """ if single := mel.ndim == 2: mel = mel.unsqueeze(0) if kwargs: options = replace(options, **kwargs) result = DecodingTask(model, options).run(mel) return result[0] if single else result @torch.no_grad() def decode1(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> 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: torch.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).run1(mel) if single: result = result[0] return result @torch.no_grad() def decode_wer(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> 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: torch.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).run_wer(mel) if single: result = result[0] return result @torch.no_grad() def decode_score(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> 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: torch.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) texts, scores = DecodingTask(model, options).run_score(mel) if single: texts = texts[0] scores = scores[0] return texts, scores