mirror of
https://github.com/openai/whisper.git
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248b6cb124
prompt_reset_since is set before all_tokens is extended hence does not have the expected effect.
458 lines
21 KiB
Python
458 lines
21 KiB
Python
import argparse
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import os
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import warnings
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from typing import TYPE_CHECKING, Optional, Tuple, Union
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import numpy as np
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import torch
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import tqdm
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from .audio import (
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FRAMES_PER_SECOND,
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HOP_LENGTH,
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N_FRAMES,
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N_SAMPLES,
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SAMPLE_RATE,
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log_mel_spectrogram,
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pad_or_trim,
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)
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from .decoding import DecodingOptions, DecodingResult
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from .timing import add_word_timestamps
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from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
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from .utils import (
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exact_div,
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format_timestamp,
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get_writer,
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make_safe,
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optional_float,
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optional_int,
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str2bool,
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)
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if TYPE_CHECKING:
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from .model import Whisper
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def transcribe(
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model: "Whisper",
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audio: Union[str, np.ndarray, torch.Tensor],
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*,
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verbose: Optional[bool] = None,
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temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
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compression_ratio_threshold: Optional[float] = 2.4,
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logprob_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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initial_prompt: Optional[str] = None,
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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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**decode_options,
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):
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"""
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Transcribe an audio file using Whisper
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Parameters
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----------
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model: Whisper
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The Whisper model instance
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audio: Union[str, np.ndarray, torch.Tensor]
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The path to the audio file to open, or the audio waveform
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verbose: bool
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Whether to display the text being decoded to the console. If True, displays all the details,
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If False, displays minimal details. If None, does not display anything
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temperature: Union[float, Tuple[float, ...]]
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Temperature for sampling. It can be a tuple of temperatures, which will be successively used
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upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
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compression_ratio_threshold: float
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If the gzip compression ratio is above this value, treat as failed
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logprob_threshold: float
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If the average log probability over sampled tokens is below this value, treat as failed
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no_speech_threshold: float
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If the no_speech probability is higher than this value AND the average log probability
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over sampled tokens is below `logprob_threshold`, consider the segment as silent
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condition_on_previous_text: bool
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if True, the previous output of the model is provided as a prompt for the next window;
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disabling may make the text inconsistent across windows, but the model becomes less prone to
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getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
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word_timestamps: bool
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Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
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and include the timestamps for each word in each segment.
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prepend_punctuations: str
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If word_timestamps is True, merge these punctuation symbols with the next word
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append_punctuations: str
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If word_timestamps is True, merge these punctuation symbols with the previous word
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initial_prompt: Optional[str]
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Optional text to provide as a prompt for the first window. This can be used to provide, or
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"prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
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to make it more likely to predict those word correctly.
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decode_options: dict
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Keyword arguments to construct `DecodingOptions` instances
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Returns
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-------
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A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
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the spoken language ("language"), which is detected when `decode_options["language"]` is None.
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"""
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dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
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if model.device == torch.device("cpu"):
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if torch.cuda.is_available():
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warnings.warn("Performing inference on CPU when CUDA is available")
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if dtype == torch.float16:
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warnings.warn("FP16 is not supported on CPU; using FP32 instead")
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dtype = torch.float32
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if dtype == torch.float32:
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decode_options["fp16"] = False
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# Pad 30-seconds of silence to the input audio, for slicing
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mel = log_mel_spectrogram(audio, padding=N_SAMPLES)
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content_frames = mel.shape[-1] - N_FRAMES
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if decode_options.get("language", None) is None:
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if not model.is_multilingual:
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decode_options["language"] = "en"
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else:
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if verbose:
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print(
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"Detecting language using up to the first 30 seconds. Use `--language` to specify the language"
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)
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mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
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_, probs = model.detect_language(mel_segment)
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decode_options["language"] = max(probs, key=probs.get)
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if verbose is not None:
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print(
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f"Detected language: {LANGUAGES[decode_options['language']].title()}"
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)
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language: str = decode_options["language"]
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task: str = decode_options.get("task", "transcribe")
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tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task)
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if word_timestamps and task == "translate":
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warnings.warn("Word-level timestamps on translations may not be reliable.")
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def decode_with_fallback(segment: torch.Tensor) -> DecodingResult:
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temperatures = (
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[temperature] if isinstance(temperature, (int, float)) else temperature
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)
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decode_result = None
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for t in temperatures:
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kwargs = {**decode_options}
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if t > 0:
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# disable beam_size and patience when t > 0
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kwargs.pop("beam_size", None)
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kwargs.pop("patience", None)
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else:
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# disable best_of when t == 0
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kwargs.pop("best_of", None)
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options = DecodingOptions(**kwargs, temperature=t)
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decode_result = model.decode(segment, options)
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needs_fallback = False
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if (
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compression_ratio_threshold is not None
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and decode_result.compression_ratio > compression_ratio_threshold
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):
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needs_fallback = True # too repetitive
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if (
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logprob_threshold is not None
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and decode_result.avg_logprob < logprob_threshold
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):
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needs_fallback = True # average log probability is too low
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if (
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no_speech_threshold is not None
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and decode_result.no_speech_prob > no_speech_threshold
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):
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needs_fallback = False # silence
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if not needs_fallback:
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break
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return decode_result
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seek = 0
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input_stride = exact_div(
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N_FRAMES, model.dims.n_audio_ctx
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) # mel frames per output token: 2
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time_precision = (
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input_stride * HOP_LENGTH / SAMPLE_RATE
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) # time per output token: 0.02 (seconds)
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all_tokens = []
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all_segments = []
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prompt_reset_since = 0
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if initial_prompt is not None:
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initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
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all_tokens.extend(initial_prompt_tokens)
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else:
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initial_prompt_tokens = []
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def new_segment(
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*, start: float, end: float, tokens: torch.Tensor, result: DecodingResult
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):
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tokens = tokens.tolist()
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text_tokens = [token for token in tokens if token < tokenizer.eot]
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return {
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"seek": seek,
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"start": start,
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"end": end,
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"text": tokenizer.decode(text_tokens),
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"tokens": tokens,
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"temperature": result.temperature,
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"avg_logprob": result.avg_logprob,
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"compression_ratio": result.compression_ratio,
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"no_speech_prob": result.no_speech_prob,
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}
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# show the progress bar when verbose is False (if True, transcribed text will be printed)
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with tqdm.tqdm(
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total=content_frames, unit="frames", disable=verbose is not False
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) as pbar:
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while seek < content_frames:
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time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
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mel_segment = mel[:, seek : seek + N_FRAMES]
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segment_size = min(N_FRAMES, content_frames - seek)
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segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
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mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype)
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decode_options["prompt"] = all_tokens[prompt_reset_since:]
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result: DecodingResult = decode_with_fallback(mel_segment)
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tokens = torch.tensor(result.tokens)
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if no_speech_threshold is not None:
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# no voice activity check
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should_skip = result.no_speech_prob > no_speech_threshold
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if (
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logprob_threshold is not None
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and result.avg_logprob > logprob_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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seek += segment_size # fast-forward to the next segment boundary
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continue
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previous_seek = seek
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current_segments = []
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timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin)
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single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True]
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consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0]
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consecutive.add_(1)
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if len(consecutive) > 0:
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# if the output contains two consecutive timestamp tokens
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slices = consecutive.tolist()
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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_pos = (
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sliced_tokens[0].item() - tokenizer.timestamp_begin
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)
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end_timestamp_pos = (
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sliced_tokens[-1].item() - tokenizer.timestamp_begin
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)
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current_segments.append(
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new_segment(
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start=time_offset + start_timestamp_pos * time_precision,
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end=time_offset + end_timestamp_pos * time_precision,
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tokens=sliced_tokens,
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result=result,
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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_pos = (
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tokens[last_slice - 1].item() - tokenizer.timestamp_begin
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)
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seek += last_timestamp_pos * input_stride
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else:
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duration = segment_duration
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timestamps = tokens[timestamp_tokens.nonzero().flatten()]
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if (
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len(timestamps) > 0
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and timestamps[-1].item() != tokenizer.timestamp_begin
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):
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# no consecutive timestamps but it has a timestamp; use the last one.
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last_timestamp_pos = (
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timestamps[-1].item() - tokenizer.timestamp_begin
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)
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duration = last_timestamp_pos * time_precision
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current_segments.append(
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new_segment(
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start=time_offset,
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end=time_offset + duration,
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tokens=tokens,
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result=result,
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)
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)
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seek += segment_size
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if word_timestamps:
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add_word_timestamps(
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segments=current_segments,
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model=model,
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tokenizer=tokenizer,
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mel=mel_segment,
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num_frames=segment_size,
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prepend_punctuations=prepend_punctuations,
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append_punctuations=append_punctuations,
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)
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word_end_timestamps = [
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w["end"] for s in current_segments for w in s["words"]
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]
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if not single_timestamp_ending and len(word_end_timestamps) > 0:
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seek_shift = round(
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(word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND
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)
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if seek_shift > 0:
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seek = previous_seek + seek_shift
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if verbose:
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for segment in current_segments:
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start, end, text = segment["start"], segment["end"], segment["text"]
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line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}"
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print(make_safe(line))
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# if a segment is instantaneous or does not contain text, clear it
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for i, segment in enumerate(current_segments):
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if segment["start"] == segment["end"] or segment["text"].strip() == "":
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segment["text"] = ""
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segment["tokens"] = []
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segment["words"] = []
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all_segments.extend(
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[
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{"id": i, **segment}
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for i, segment in enumerate(
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current_segments, start=len(all_segments)
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)
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]
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)
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all_tokens.extend(
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[token for segment in current_segments for token in segment["tokens"]]
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)
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if not condition_on_previous_text or result.temperature > 0.5:
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# do not feed the prompt tokens if a high temperature was used
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prompt_reset_since = len(all_tokens)
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# update progress bar
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pbar.update(min(content_frames, seek) - previous_seek)
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return dict(
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text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]),
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segments=all_segments,
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language=language,
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)
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def cli():
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from . import available_models
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# fmt: off
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
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parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use")
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parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
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parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
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parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
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parser.add_argument("--output_format", "-f", type=str, default="all", choices=["txt", "vtt", "srt", "tsv", "json", "all"], help="format of the output file; if not specified, all available formats will be produced")
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parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
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parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
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parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")
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parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
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parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
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parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
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parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search")
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parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default")
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parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
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parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
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parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model 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")
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parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
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parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
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parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
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parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
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parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
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parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them")
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parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word")
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parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word")
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parser.add_argument("--highlight_words", type=str2bool, default=False, help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt")
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parser.add_argument("--max_line_width", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of characters in a line before breaking the line")
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parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of lines in a segment")
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parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
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# fmt: on
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args = parser.parse_args().__dict__
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model_name: str = args.pop("model")
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model_dir: str = args.pop("model_dir")
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output_dir: str = args.pop("output_dir")
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output_format: str = args.pop("output_format")
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device: str = args.pop("device")
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os.makedirs(output_dir, exist_ok=True)
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if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
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if args["language"] is not None:
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warnings.warn(
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f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead."
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)
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args["language"] = "en"
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temperature = args.pop("temperature")
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if (increment := args.pop("temperature_increment_on_fallback")) is not None:
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temperature = tuple(np.arange(temperature, 1.0 + 1e-6, increment))
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else:
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temperature = [temperature]
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if (threads := args.pop("threads")) > 0:
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torch.set_num_threads(threads)
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from . import load_model
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model = load_model(model_name, device=device, download_root=model_dir)
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writer = get_writer(output_format, output_dir)
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word_options = ["highlight_words", "max_line_count", "max_line_width"]
|
||
if not args["word_timestamps"]:
|
||
for option in word_options:
|
||
if args[option]:
|
||
parser.error(f"--{option} requires --word_timestamps True")
|
||
if args["max_line_count"] and not args["max_line_width"]:
|
||
warnings.warn("--max_line_count has no effect without --max_line_width")
|
||
writer_args = {arg: args.pop(arg) for arg in word_options}
|
||
for audio_path in args.pop("audio"):
|
||
result = transcribe(model, audio_path, temperature=temperature, **args)
|
||
writer(result, audio_path, writer_args)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
cli()
|