e87fbf8a49
* Added VAD removed audio duration to TranscriptionInfo object Along with the duration of the original audio, this commit adds the seconds of audio removed by the VAD to the returned info obj * Chaning naming for duration_after_vad Instead of the property returning the audio duration removed, it now returns the final duration after the vad. If vad_filter is False or if it doesn't remove any audio, the original duration is returned.
1000 lines
38 KiB
Python
1000 lines
38 KiB
Python
import itertools
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import logging
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import os
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import zlib
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from typing import BinaryIO, Iterable, List, NamedTuple, Optional, Tuple, Union
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import ctranslate2
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import numpy as np
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import tokenizers
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from faster_whisper.audio import decode_audio
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from faster_whisper.feature_extractor import FeatureExtractor
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from faster_whisper.tokenizer import Tokenizer
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from faster_whisper.utils import download_model, format_timestamp, get_logger
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from faster_whisper.vad import (
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SpeechTimestampsMap,
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VadOptions,
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collect_chunks,
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get_speech_timestamps,
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)
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class Word(NamedTuple):
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start: float
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end: float
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word: str
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probability: float
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class Segment(NamedTuple):
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id: int
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seek: int
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start: float
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end: float
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text: str
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tokens: List[int]
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temperature: float
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avg_logprob: float
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compression_ratio: float
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no_speech_prob: float
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words: Optional[List[Word]]
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class TranscriptionOptions(NamedTuple):
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beam_size: int
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best_of: int
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patience: float
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length_penalty: float
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repetition_penalty: float
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log_prob_threshold: Optional[float]
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no_speech_threshold: Optional[float]
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compression_ratio_threshold: Optional[float]
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condition_on_previous_text: bool
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prompt_reset_on_temperature: float
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temperatures: List[float]
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initial_prompt: Optional[Union[str, Iterable[int]]]
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prefix: Optional[str]
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suppress_blank: bool
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suppress_tokens: Optional[List[int]]
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without_timestamps: bool
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max_initial_timestamp: float
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word_timestamps: bool
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prepend_punctuations: str
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append_punctuations: str
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class TranscriptionInfo(NamedTuple):
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language: str
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language_probability: float
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duration: float
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duration_after_vad: float
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all_language_probs: Optional[List[Tuple[str, float]]]
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transcription_options: TranscriptionOptions
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vad_options: VadOptions
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class WhisperModel:
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def __init__(
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self,
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model_size_or_path: str,
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device: str = "auto",
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device_index: Union[int, List[int]] = 0,
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compute_type: str = "default",
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cpu_threads: int = 0,
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num_workers: int = 1,
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download_root: Optional[str] = None,
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local_files_only: bool = False,
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):
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"""Initializes the Whisper model.
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Args:
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model_size_or_path: Size of the model to use (tiny, tiny.en, base, base.en,
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small, small.en, medium, medium.en, large-v1, or large-v2), a path to a converted
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model directory, or a CTranslate2-converted Whisper model ID from the Hugging Face Hub.
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When a size or a model ID is configured, the converted model is downloaded
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from the Hugging Face Hub.
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device: Device to use for computation ("cpu", "cuda", "auto").
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device_index: Device ID to use.
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The model can also be loaded on multiple GPUs by passing a list of IDs
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(e.g. [0, 1, 2, 3]). In that case, multiple transcriptions can run in parallel
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when transcribe() is called from multiple Python threads (see also num_workers).
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compute_type: Type to use for computation.
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See https://opennmt.net/CTranslate2/quantization.html.
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cpu_threads: Number of threads to use when running on CPU (4 by default).
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A non zero value overrides the OMP_NUM_THREADS environment variable.
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num_workers: When transcribe() is called from multiple Python threads,
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having multiple workers enables true parallelism when running the model
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(concurrent calls to self.model.generate() will run in parallel).
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This can improve the global throughput at the cost of increased memory usage.
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download_root: Directory where the models should be saved. If not set, the models
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are saved in the standard Hugging Face cache directory.
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local_files_only: If True, avoid downloading the file and return the path to the
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local cached file if it exists.
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"""
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self.logger = get_logger()
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if os.path.isdir(model_size_or_path):
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model_path = model_size_or_path
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else:
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model_path = download_model(
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model_size_or_path,
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local_files_only=local_files_only,
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cache_dir=download_root,
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)
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self.model = ctranslate2.models.Whisper(
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model_path,
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device=device,
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device_index=device_index,
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compute_type=compute_type,
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intra_threads=cpu_threads,
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inter_threads=num_workers,
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)
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tokenizer_file = os.path.join(model_path, "tokenizer.json")
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if os.path.isfile(tokenizer_file):
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self.hf_tokenizer = tokenizers.Tokenizer.from_file(tokenizer_file)
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else:
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self.hf_tokenizer = tokenizers.Tokenizer.from_pretrained(
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"openai/whisper-tiny" + ("" if self.model.is_multilingual else ".en")
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)
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self.feature_extractor = FeatureExtractor()
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self.num_samples_per_token = self.feature_extractor.hop_length * 2
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self.frames_per_second = (
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self.feature_extractor.sampling_rate // self.feature_extractor.hop_length
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)
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self.tokens_per_second = (
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self.feature_extractor.sampling_rate // self.num_samples_per_token
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)
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self.input_stride = 2
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self.time_precision = 0.02
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self.max_length = 448
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def transcribe(
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self,
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audio: Union[str, BinaryIO, np.ndarray],
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language: Optional[str] = None,
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task: str = "transcribe",
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beam_size: int = 5,
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best_of: int = 5,
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patience: float = 1,
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length_penalty: float = 1,
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repetition_penalty: float = 1,
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temperature: Union[float, List[float], Tuple[float, ...]] = [
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0.0,
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0.2,
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0.4,
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0.6,
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0.8,
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1.0,
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],
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compression_ratio_threshold: Optional[float] = 2.4,
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log_prob_threshold: Optional[float] = -1.0,
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no_speech_threshold: Optional[float] = 0.6,
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condition_on_previous_text: bool = True,
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prompt_reset_on_temperature: float = 0.5,
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initial_prompt: Optional[Union[str, Iterable[int]]] = None,
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prefix: Optional[str] = None,
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suppress_blank: bool = True,
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suppress_tokens: Optional[List[int]] = [-1],
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without_timestamps: bool = False,
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max_initial_timestamp: float = 1.0,
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word_timestamps: bool = False,
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prepend_punctuations: str = "\"'“¿([{-",
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append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
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vad_filter: bool = False,
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vad_parameters: Optional[Union[dict, VadOptions]] = None,
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) -> Tuple[Iterable[Segment], TranscriptionInfo]:
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"""Transcribes an input file.
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Arguments:
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audio: Path to the input file (or a file-like object), or the audio waveform.
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language: The language spoken in the audio. It should be a language code such
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as "en" or "fr". If not set, the language will be detected in the first 30 seconds
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of audio.
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task: Task to execute (transcribe or translate).
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beam_size: Beam size to use for decoding.
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best_of: Number of candidates when sampling with non-zero temperature.
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patience: Beam search patience factor.
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length_penalty: Exponential length penalty constant.
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repetition_penalty: Penalty applied to the score of previously generated tokens
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(set > 1 to penalize).
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temperature: Temperature for sampling. It can be a tuple of temperatures,
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which will be successively used upon failures according to either
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`compression_ratio_threshold` or `log_prob_threshold`.
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compression_ratio_threshold: If the gzip compression ratio is above this value,
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treat as failed.
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log_prob_threshold: If the average log probability over sampled tokens is
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below this value, treat as failed.
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no_speech_threshold: If the no_speech probability is higher than this value AND
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the average log probability over sampled tokens is below `log_prob_threshold`,
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consider the segment as silent.
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condition_on_previous_text: If True, the previous output of the model is provided
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as a prompt for the next window; disabling may make the text inconsistent across
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windows, but the model becomes less prone to getting stuck in a failure loop,
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such as repetition looping or timestamps going out of sync.
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prompt_reset_on_temperature: Resets prompt if temperature is above this value.
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Arg has effect only if condition_on_previous_text is True.
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initial_prompt: Optional text string or iterable of token ids to provide as a
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prompt for the first window.
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prefix: Optional text to provide as a prefix for the first window.
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suppress_blank: Suppress blank outputs at the beginning of the sampling.
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suppress_tokens: List of token IDs to suppress. -1 will suppress a default set
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of symbols as defined in the model config.json file.
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without_timestamps: Only sample text tokens.
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max_initial_timestamp: The initial timestamp cannot be later than this.
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word_timestamps: Extract word-level timestamps using the cross-attention pattern
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and dynamic time warping, and include the timestamps for each word in each segment.
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prepend_punctuations: If word_timestamps is True, merge these punctuation symbols
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with the next word
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append_punctuations: If word_timestamps is True, merge these punctuation symbols
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with the previous word
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vad_filter: Enable the voice activity detection (VAD) to filter out parts of the audio
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without speech. This step is using the Silero VAD model
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https://github.com/snakers4/silero-vad.
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vad_parameters: Dictionary of Silero VAD parameters or VadOptions class (see available
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parameters and default values in the class `VadOptions`).
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Returns:
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A tuple with:
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- a generator over transcribed segments
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- an instance of TranscriptionInfo
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"""
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sampling_rate = self.feature_extractor.sampling_rate
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if not isinstance(audio, np.ndarray):
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audio = decode_audio(audio, sampling_rate=sampling_rate)
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duration = audio.shape[0] / sampling_rate
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duration_after_vad = duration
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self.logger.info(
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"Processing audio with duration %s", format_timestamp(duration)
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)
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if vad_filter:
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if vad_parameters is None:
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vad_parameters = VadOptions()
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elif isinstance(vad_parameters, dict):
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vad_parameters = VadOptions(**vad_parameters)
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speech_chunks = get_speech_timestamps(audio, vad_parameters)
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audio = collect_chunks(audio, speech_chunks)
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duration_after_vad = audio.shape[0] / sampling_rate
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self.logger.info(
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"VAD filter removed %s of audio",
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format_timestamp(duration - duration_after_vad),
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)
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if self.logger.isEnabledFor(logging.DEBUG):
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self.logger.debug(
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"VAD filter kept the following audio segments: %s",
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", ".join(
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"[%s -> %s]"
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% (
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format_timestamp(chunk["start"] / sampling_rate),
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format_timestamp(chunk["end"] / sampling_rate),
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)
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for chunk in speech_chunks
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),
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)
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else:
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speech_chunks = None
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features = self.feature_extractor(audio)
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encoder_output = None
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all_language_probs = None
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if language is None:
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if not self.model.is_multilingual:
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language = "en"
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language_probability = 1
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else:
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segment = features[:, : self.feature_extractor.nb_max_frames]
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encoder_output = self.encode(segment)
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# results is a list of tuple[str, float] with language names and
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# probabilities.
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results = self.model.detect_language(encoder_output)[0]
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# Parse language names to strip out markers
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all_language_probs = [(token[2:-2], prob) for (token, prob) in results]
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# Get top language token and probability
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language, language_probability = all_language_probs[0]
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self.logger.info(
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"Detected language '%s' with probability %.2f",
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language,
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language_probability,
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)
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else:
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language_probability = 1
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tokenizer = Tokenizer(
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self.hf_tokenizer,
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self.model.is_multilingual,
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task=task,
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language=language,
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)
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options = TranscriptionOptions(
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beam_size=beam_size,
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best_of=best_of,
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patience=patience,
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length_penalty=length_penalty,
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repetition_penalty=repetition_penalty,
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log_prob_threshold=log_prob_threshold,
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no_speech_threshold=no_speech_threshold,
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compression_ratio_threshold=compression_ratio_threshold,
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condition_on_previous_text=condition_on_previous_text,
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prompt_reset_on_temperature=prompt_reset_on_temperature,
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temperatures=(
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temperature if isinstance(temperature, (list, tuple)) else [temperature]
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),
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initial_prompt=initial_prompt,
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prefix=prefix,
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suppress_blank=suppress_blank,
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suppress_tokens=get_suppressed_tokens(tokenizer, suppress_tokens),
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without_timestamps=without_timestamps,
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max_initial_timestamp=max_initial_timestamp,
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word_timestamps=word_timestamps,
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prepend_punctuations=prepend_punctuations,
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append_punctuations=append_punctuations,
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)
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segments = self.generate_segments(features, tokenizer, options, encoder_output)
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if speech_chunks:
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segments = restore_speech_timestamps(segments, speech_chunks, sampling_rate)
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info = TranscriptionInfo(
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language=language,
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language_probability=language_probability,
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duration=duration,
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duration_after_vad=duration_after_vad,
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transcription_options=options,
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vad_options=vad_parameters,
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all_language_probs=all_language_probs,
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)
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return segments, info
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def generate_segments(
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self,
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features: np.ndarray,
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tokenizer: Tokenizer,
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options: TranscriptionOptions,
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encoder_output: Optional[ctranslate2.StorageView] = None,
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) -> Iterable[Segment]:
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content_frames = features.shape[-1] - self.feature_extractor.nb_max_frames
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idx = 0
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seek = 0
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all_tokens = []
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prompt_reset_since = 0
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if options.initial_prompt is not None:
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if isinstance(options.initial_prompt, str):
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initial_prompt = " " + options.initial_prompt.strip()
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initial_prompt_tokens = tokenizer.encode(initial_prompt)
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all_tokens.extend(initial_prompt_tokens)
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else:
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all_tokens.extend(options.initial_prompt)
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last_speech_timestamp = 0.0
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while seek < content_frames:
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time_offset = seek * self.feature_extractor.time_per_frame
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segment = features[:, seek : seek + self.feature_extractor.nb_max_frames]
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segment_size = min(
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self.feature_extractor.nb_max_frames, content_frames - seek
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)
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segment_duration = segment_size * self.feature_extractor.time_per_frame
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if self.logger.isEnabledFor(logging.DEBUG):
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self.logger.debug(
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"Processing segment at %s", format_timestamp(time_offset)
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)
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previous_tokens = all_tokens[prompt_reset_since:]
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prompt = self.get_prompt(
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tokenizer,
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previous_tokens,
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without_timestamps=options.without_timestamps,
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prefix=options.prefix if seek == 0 else None,
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)
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if encoder_output is None:
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encoder_output = self.encode(segment)
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(
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result,
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avg_logprob,
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temperature,
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compression_ratio,
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) = self.generate_with_fallback(encoder_output, prompt, tokenizer, options)
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if options.no_speech_threshold is not None:
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# no voice activity check
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should_skip = result.no_speech_prob > options.no_speech_threshold
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if (
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options.log_prob_threshold is not None
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and avg_logprob > options.log_prob_threshold
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):
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# don't skip if the logprob is high enough, despite the no_speech_prob
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should_skip = False
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if should_skip:
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self.logger.debug(
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"No speech threshold is met (%f > %f)",
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result.no_speech_prob,
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options.no_speech_threshold,
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)
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# fast-forward to the next segment boundary
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seek += segment_size
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encoder_output = None
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continue
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tokens = result.sequences_ids[0]
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previous_seek = seek
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current_segments = []
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single_timestamp_ending = (
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len(tokens) >= 2
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and tokens[-2] < tokenizer.timestamp_begin
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and tokens[-1] >= tokenizer.timestamp_begin
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)
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consecutive_timestamps = [
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i
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for i in range(len(tokens))
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if i > 0
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and tokens[i] >= tokenizer.timestamp_begin
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and tokens[i - 1] >= tokenizer.timestamp_begin
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]
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if len(consecutive_timestamps) > 0:
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slices = list(consecutive_timestamps)
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if single_timestamp_ending:
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slices.append(len(tokens))
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last_slice = 0
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for current_slice in slices:
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sliced_tokens = tokens[last_slice:current_slice]
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start_timestamp_position = (
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sliced_tokens[0] - tokenizer.timestamp_begin
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)
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end_timestamp_position = (
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sliced_tokens[-1] - tokenizer.timestamp_begin
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)
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start_time = (
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time_offset + start_timestamp_position * self.time_precision
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)
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end_time = (
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time_offset + end_timestamp_position * self.time_precision
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)
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current_segments.append(
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dict(
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seek=seek,
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start=start_time,
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end=end_time,
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tokens=sliced_tokens,
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)
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)
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last_slice = current_slice
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if single_timestamp_ending:
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# single timestamp at the end means no speech after the last timestamp.
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seek += segment_size
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else:
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# otherwise, ignore the unfinished segment and seek to the last timestamp
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last_timestamp_position = (
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tokens[last_slice - 1] - tokenizer.timestamp_begin
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)
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seek += last_timestamp_position * self.input_stride
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else:
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duration = segment_duration
|
||
timestamps = [
|
||
token for token in tokens if token >= tokenizer.timestamp_begin
|
||
]
|
||
if len(timestamps) > 0 and timestamps[-1] != tokenizer.timestamp_begin:
|
||
last_timestamp_position = timestamps[-1] - tokenizer.timestamp_begin
|
||
duration = last_timestamp_position * self.time_precision
|
||
|
||
current_segments.append(
|
||
dict(
|
||
seek=seek,
|
||
start=time_offset,
|
||
end=time_offset + duration,
|
||
tokens=tokens,
|
||
)
|
||
)
|
||
|
||
seek += segment_size
|
||
|
||
if options.word_timestamps:
|
||
self.add_word_timestamps(
|
||
current_segments,
|
||
tokenizer,
|
||
encoder_output,
|
||
segment_size,
|
||
options.prepend_punctuations,
|
||
options.append_punctuations,
|
||
last_speech_timestamp=last_speech_timestamp,
|
||
)
|
||
|
||
word_end_timestamps = [
|
||
w["end"] for s in current_segments for w in s["words"]
|
||
]
|
||
if len(word_end_timestamps) > 0:
|
||
last_speech_timestamp = word_end_timestamps[-1]
|
||
if not single_timestamp_ending and len(word_end_timestamps) > 0:
|
||
seek_shift = round(
|
||
(word_end_timestamps[-1] - time_offset) * self.frames_per_second
|
||
)
|
||
|
||
if seek_shift > 0:
|
||
seek = previous_seek + seek_shift
|
||
|
||
encoder_output = None
|
||
|
||
for segment in current_segments:
|
||
tokens = segment["tokens"]
|
||
text = tokenizer.decode(tokens)
|
||
|
||
if segment["start"] == segment["end"] or not text.strip():
|
||
continue
|
||
|
||
all_tokens.extend(tokens)
|
||
idx += 1
|
||
|
||
yield Segment(
|
||
id=idx,
|
||
seek=seek,
|
||
start=segment["start"],
|
||
end=segment["end"],
|
||
text=text,
|
||
tokens=tokens,
|
||
temperature=temperature,
|
||
avg_logprob=avg_logprob,
|
||
compression_ratio=compression_ratio,
|
||
no_speech_prob=result.no_speech_prob,
|
||
words=(
|
||
[Word(**word) for word in segment["words"]]
|
||
if options.word_timestamps
|
||
else None
|
||
),
|
||
)
|
||
|
||
if (
|
||
not options.condition_on_previous_text
|
||
or temperature > options.prompt_reset_on_temperature
|
||
):
|
||
if options.condition_on_previous_text:
|
||
self.logger.debug(
|
||
"Reset prompt. prompt_reset_on_temperature threshold is met %f > %f",
|
||
temperature,
|
||
options.prompt_reset_on_temperature,
|
||
)
|
||
|
||
prompt_reset_since = len(all_tokens)
|
||
|
||
def encode(self, features: np.ndarray) -> ctranslate2.StorageView:
|
||
# When the model is running on multiple GPUs, the encoder output should be moved
|
||
# to the CPU since we don't know which GPU will handle the next job.
|
||
to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
|
||
|
||
features = np.expand_dims(features, 0)
|
||
features = get_ctranslate2_storage(features)
|
||
|
||
return self.model.encode(features, to_cpu=to_cpu)
|
||
|
||
def generate_with_fallback(
|
||
self,
|
||
encoder_output: ctranslate2.StorageView,
|
||
prompt: List[int],
|
||
tokenizer: Tokenizer,
|
||
options: TranscriptionOptions,
|
||
) -> Tuple[ctranslate2.models.WhisperGenerationResult, float, float, float]:
|
||
decode_result = None
|
||
all_results = []
|
||
below_cr_threshold_results = []
|
||
|
||
max_initial_timestamp_index = int(
|
||
round(options.max_initial_timestamp / self.time_precision)
|
||
)
|
||
|
||
for temperature in options.temperatures:
|
||
if temperature > 0:
|
||
kwargs = {
|
||
"beam_size": 1,
|
||
"num_hypotheses": options.best_of,
|
||
"sampling_topk": 0,
|
||
"sampling_temperature": temperature,
|
||
}
|
||
else:
|
||
kwargs = {
|
||
"beam_size": options.beam_size,
|
||
"patience": options.patience,
|
||
}
|
||
|
||
result = self.model.generate(
|
||
encoder_output,
|
||
[prompt],
|
||
length_penalty=options.length_penalty,
|
||
repetition_penalty=options.repetition_penalty,
|
||
max_length=self.max_length,
|
||
return_scores=True,
|
||
return_no_speech_prob=True,
|
||
suppress_blank=options.suppress_blank,
|
||
suppress_tokens=options.suppress_tokens,
|
||
max_initial_timestamp_index=max_initial_timestamp_index,
|
||
**kwargs,
|
||
)[0]
|
||
|
||
tokens = result.sequences_ids[0]
|
||
|
||
# Recover the average log prob from the returned score.
|
||
seq_len = len(tokens)
|
||
cum_logprob = result.scores[0] * (seq_len**options.length_penalty)
|
||
avg_logprob = cum_logprob / (seq_len + 1)
|
||
|
||
text = tokenizer.decode(tokens).strip()
|
||
compression_ratio = get_compression_ratio(text)
|
||
|
||
decode_result = (
|
||
result,
|
||
avg_logprob,
|
||
temperature,
|
||
compression_ratio,
|
||
)
|
||
all_results.append(decode_result)
|
||
|
||
needs_fallback = False
|
||
|
||
if options.compression_ratio_threshold is not None:
|
||
if compression_ratio > options.compression_ratio_threshold:
|
||
needs_fallback = True # too repetitive
|
||
|
||
self.logger.debug(
|
||
"Compression ratio threshold is not met with temperature %.1f (%f > %f)",
|
||
temperature,
|
||
compression_ratio,
|
||
options.compression_ratio_threshold,
|
||
)
|
||
else:
|
||
below_cr_threshold_results.append(decode_result)
|
||
|
||
if (
|
||
options.log_prob_threshold is not None
|
||
and avg_logprob < options.log_prob_threshold
|
||
):
|
||
needs_fallback = True # average log probability is too low
|
||
|
||
self.logger.debug(
|
||
"Log probability threshold is not met with temperature %.1f (%f < %f)",
|
||
temperature,
|
||
avg_logprob,
|
||
options.log_prob_threshold,
|
||
)
|
||
|
||
if (
|
||
options.no_speech_threshold is not None
|
||
and result.no_speech_prob > options.no_speech_threshold
|
||
):
|
||
needs_fallback = False # silence
|
||
|
||
if not needs_fallback:
|
||
break
|
||
else:
|
||
# all failed, select the result with the highest average log probability
|
||
decode_result = max(
|
||
below_cr_threshold_results or all_results, key=lambda x: x[1]
|
||
)
|
||
|
||
return decode_result
|
||
|
||
def get_prompt(
|
||
self,
|
||
tokenizer: Tokenizer,
|
||
previous_tokens: List[int],
|
||
without_timestamps: bool = False,
|
||
prefix: Optional[str] = None,
|
||
) -> List[int]:
|
||
prompt = []
|
||
|
||
if previous_tokens:
|
||
prompt.append(tokenizer.sot_prev)
|
||
prompt.extend(previous_tokens[-(self.max_length // 2 - 1) :])
|
||
|
||
prompt.extend(tokenizer.sot_sequence)
|
||
|
||
if without_timestamps:
|
||
prompt.append(tokenizer.no_timestamps)
|
||
|
||
if prefix:
|
||
prefix_tokens = tokenizer.encode(" " + prefix.strip())
|
||
if len(prefix_tokens) >= self.max_length // 2:
|
||
prefix_tokens = prefix_tokens[: self.max_length // 2 - 1]
|
||
if not without_timestamps:
|
||
prompt.append(tokenizer.timestamp_begin)
|
||
prompt.extend(prefix_tokens)
|
||
|
||
return prompt
|
||
|
||
def add_word_timestamps(
|
||
self,
|
||
segments: List[dict],
|
||
tokenizer: Tokenizer,
|
||
encoder_output: ctranslate2.StorageView,
|
||
num_frames: int,
|
||
prepend_punctuations: str,
|
||
append_punctuations: str,
|
||
last_speech_timestamp: float,
|
||
):
|
||
if len(segments) == 0:
|
||
return
|
||
|
||
text_tokens_per_segment = [
|
||
[token for token in segment["tokens"] if token < tokenizer.eot]
|
||
for segment in segments
|
||
]
|
||
|
||
text_tokens = list(itertools.chain.from_iterable(text_tokens_per_segment))
|
||
alignment = self.find_alignment(
|
||
tokenizer, text_tokens, encoder_output, num_frames
|
||
)
|
||
word_durations = np.array([word["end"] - word["start"] for word in alignment])
|
||
word_durations = word_durations[word_durations.nonzero()]
|
||
median_duration = np.median(word_durations) if len(word_durations) > 0 else 0.0
|
||
max_duration = median_duration * 2
|
||
|
||
# hack: truncate long words at sentence boundaries.
|
||
# a better segmentation algorithm based on VAD should be able to replace this.
|
||
if len(word_durations) > 0:
|
||
sentence_end_marks = ".。!！?？"
|
||
# ensure words at sentence boundaries
|
||
# are not longer than twice the median word duration.
|
||
for i in range(1, len(alignment)):
|
||
if alignment[i]["end"] - alignment[i]["start"] > max_duration:
|
||
if alignment[i]["word"] in sentence_end_marks:
|
||
alignment[i]["end"] = alignment[i]["start"] + max_duration
|
||
elif alignment[i - 1]["word"] in sentence_end_marks:
|
||
alignment[i]["start"] = alignment[i]["end"] - max_duration
|
||
|
||
merge_punctuations(alignment, prepend_punctuations, append_punctuations)
|
||
|
||
time_offset = (
|
||
segments[0]["seek"]
|
||
* self.feature_extractor.hop_length
|
||
/ self.feature_extractor.sampling_rate
|
||
)
|
||
|
||
word_index = 0
|
||
|
||
for segment, text_tokens in zip(segments, text_tokens_per_segment):
|
||
saved_tokens = 0
|
||
words = []
|
||
|
||
while word_index < len(alignment) and saved_tokens < len(text_tokens):
|
||
timing = alignment[word_index]
|
||
|
||
if timing["word"]:
|
||
words.append(
|
||
dict(
|
||
word=timing["word"],
|
||
start=round(time_offset + timing["start"], 2),
|
||
end=round(time_offset + timing["end"], 2),
|
||
probability=timing["probability"],
|
||
)
|
||
)
|
||
|
||
saved_tokens += len(timing["tokens"])
|
||
word_index += 1
|
||
|
||
# hack: truncate long words at segment boundaries.
|
||
# a better segmentation algorithm based on VAD should be able to replace this.
|
||
if len(words) > 0:
|
||
# ensure the first and second word after a pause is not longer than
|
||
# twice the median word duration.
|
||
if words[0]["end"] - last_speech_timestamp > median_duration * 4 and (
|
||
words[0]["end"] - words[0]["start"] > max_duration
|
||
or (
|
||
len(words) > 1
|
||
and words[1]["end"] - words[0]["start"] > max_duration * 2
|
||
)
|
||
):
|
||
if (
|
||
len(words) > 1
|
||
and words[1]["end"] - words[1]["start"] > max_duration
|
||
):
|
||
boundary = max(
|
||
words[1]["end"] / 2, words[1]["end"] - max_duration
|
||
)
|
||
words[0]["end"] = words[1]["start"] = boundary
|
||
words[0]["start"] = max(0, words[0]["end"] - max_duration)
|
||
|
||
# prefer the segment-level start timestamp if the first word is too long.
|
||
if (
|
||
segment["start"] < words[0]["end"]
|
||
and segment["start"] - 0.5 > words[0]["start"]
|
||
):
|
||
words[0]["start"] = max(
|
||
0, min(words[0]["end"] - median_duration, segment["start"])
|
||
)
|
||
else:
|
||
segment["start"] = words[0]["start"]
|
||
|
||
# prefer the segment-level end timestamp if the last word is too long.
|
||
if (
|
||
segment["end"] > words[-1]["start"]
|
||
and segment["end"] + 0.5 < words[-1]["end"]
|
||
):
|
||
words[-1]["end"] = max(
|
||
words[-1]["start"] + median_duration, segment["end"]
|
||
)
|
||
else:
|
||
segment["end"] = words[-1]["end"]
|
||
|
||
last_speech_timestamp = segment["end"]
|
||
|
||
segment["words"] = words
|
||
|
||
def find_alignment(
|
||
self,
|
||
tokenizer: Tokenizer,
|
||
text_tokens: List[int],
|
||
encoder_output: ctranslate2.StorageView,
|
||
num_frames: int,
|
||
median_filter_width: int = 7,
|
||
) -> List[dict]:
|
||
if len(text_tokens) == 0:
|
||
return []
|
||
|
||
result = self.model.align(
|
||
encoder_output,
|
||
tokenizer.sot_sequence,
|
||
[text_tokens],
|
||
num_frames,
|
||
median_filter_width=median_filter_width,
|
||
)[0]
|
||
|
||
text_token_probs = result.text_token_probs
|
||
|
||
alignments = result.alignments
|
||
text_indices = np.array([pair[0] for pair in alignments])
|
||
time_indices = np.array([pair[1] for pair in alignments])
|
||
|
||
words, word_tokens = tokenizer.split_to_word_tokens(
|
||
text_tokens + [tokenizer.eot]
|
||
)
|
||
word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0))
|
||
if len(word_boundaries) <= 1:
|
||
return []
|
||
|
||
jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool)
|
||
jump_times = time_indices[jumps] / self.tokens_per_second
|
||
start_times = jump_times[word_boundaries[:-1]]
|
||
end_times = jump_times[word_boundaries[1:]]
|
||
word_probabilities = [
|
||
np.mean(text_token_probs[i:j])
|
||
for i, j in zip(word_boundaries[:-1], word_boundaries[1:])
|
||
]
|
||
|
||
return [
|
||
dict(
|
||
word=word, tokens=tokens, start=start, end=end, probability=probability
|
||
)
|
||
for word, tokens, start, end, probability in zip(
|
||
words, word_tokens, start_times, end_times, word_probabilities
|
||
)
|
||
]
|
||
|
||
|
||
def restore_speech_timestamps(
|
||
segments: Iterable[Segment],
|
||
speech_chunks: List[dict],
|
||
sampling_rate: int,
|
||
) -> Iterable[Segment]:
|
||
ts_map = SpeechTimestampsMap(speech_chunks, sampling_rate)
|
||
|
||
for segment in segments:
|
||
if segment.words:
|
||
words = []
|
||
for word in segment.words:
|
||
# Ensure the word start and end times are resolved to the same chunk.
|
||
middle = (word.start + word.end) / 2
|
||
chunk_index = ts_map.get_chunk_index(middle)
|
||
word = word._replace(
|
||
start=ts_map.get_original_time(word.start, chunk_index),
|
||
end=ts_map.get_original_time(word.end, chunk_index),
|
||
)
|
||
words.append(word)
|
||
|
||
segment = segment._replace(
|
||
start=words[0].start,
|
||
end=words[-1].end,
|
||
words=words,
|
||
)
|
||
|
||
else:
|
||
segment = segment._replace(
|
||
start=ts_map.get_original_time(segment.start),
|
||
end=ts_map.get_original_time(segment.end),
|
||
)
|
||
|
||
yield segment
|
||
|
||
|
||
def get_ctranslate2_storage(segment: np.ndarray) -> ctranslate2.StorageView:
|
||
segment = np.ascontiguousarray(segment)
|
||
segment = ctranslate2.StorageView.from_array(segment)
|
||
return segment
|
||
|
||
|
||
def get_compression_ratio(text: str) -> float:
|
||
text_bytes = text.encode("utf-8")
|
||
return len(text_bytes) / len(zlib.compress(text_bytes))
|
||
|
||
|
||
def get_suppressed_tokens(tokenizer, suppress_tokens):
|
||
if not suppress_tokens or -1 in suppress_tokens:
|
||
return suppress_tokens
|
||
|
||
suppress_tokens = list(suppress_tokens)
|
||
|
||
# Ensure the following special tokens are suppressed when the user does
|
||
# not use the default set (-1).
|
||
suppress_tokens.extend(
|
||
[
|
||
tokenizer.transcribe,
|
||
tokenizer.translate,
|
||
tokenizer.sot,
|
||
tokenizer.sot_prev,
|
||
tokenizer.sot_lm,
|
||
]
|
||
)
|
||
|
||
return sorted(set(suppress_tokens))
|
||
|
||
|
||
def merge_punctuations(alignment: List[dict], prepended: str, appended: str):
|
||
# merge prepended punctuations
|
||
i = len(alignment) - 2
|
||
j = len(alignment) - 1
|
||
while i >= 0:
|
||
previous = alignment[i]
|
||
following = alignment[j]
|
||
if previous["word"].startswith(" ") and previous["word"].strip() in prepended:
|
||
# prepend it to the following word
|
||
following["word"] = previous["word"] + following["word"]
|
||
following["tokens"] = previous["tokens"] + following["tokens"]
|
||
previous["word"] = ""
|
||
previous["tokens"] = []
|
||
else:
|
||
j = i
|
||
i -= 1
|
||
|
||
# merge appended punctuations
|
||
i = 0
|
||
j = 1
|
||
while j < len(alignment):
|
||
previous = alignment[i]
|
||
following = alignment[j]
|
||
if not previous["word"].endswith(" ") and following["word"] in appended:
|
||
# append it to the previous word
|
||
previous["word"] = previous["word"] + following["word"]
|
||
previous["tokens"] = previous["tokens"] + following["tokens"]
|
||
following["word"] = ""
|
||
following["tokens"] = []
|
||
else:
|
||
i = j
|
||
j += 1
|