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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # This module is modified from [Whisper](https://github.com/openai/whisper.git). | |
| # ## Citations | |
| # ```bibtex | |
| # @inproceedings{openai-whisper, | |
| # author = {Alec Radford and | |
| # Jong Wook Kim and | |
| # Tao Xu and | |
| # Greg Brockman and | |
| # Christine McLeavey and | |
| # Ilya Sutskever}, | |
| # title = {Robust Speech Recognition via Large-Scale Weak Supervision}, | |
| # booktitle = {{ICML}}, | |
| # series = {Proceedings of Machine Learning Research}, | |
| # volume = {202}, | |
| # pages = {28492--28518}, | |
| # publisher = {{PMLR}}, | |
| # year = {2023} | |
| # } | |
| # ``` | |
| # | |
| import zlib | |
| from typing import Iterator, TextIO | |
| def exact_div(x, y): | |
| assert x % y == 0 | |
| return x // y | |
| def str2bool(string): | |
| str2val = {"True": True, "False": False} | |
| if string in str2val: | |
| return str2val[string] | |
| else: | |
| raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") | |
| def optional_int(string): | |
| return None if string == "None" else int(string) | |
| def optional_float(string): | |
| return None if string == "None" else float(string) | |
| def compression_ratio(text) -> float: | |
| text_bytes = text.encode("utf-8") | |
| return len(text_bytes) / len(zlib.compress(text_bytes)) | |
| def format_timestamp( | |
| seconds: float, always_include_hours: bool = False, decimal_marker: str = "." | |
| ): | |
| assert seconds >= 0, "non-negative timestamp expected" | |
| milliseconds = round(seconds * 1000.0) | |
| hours = milliseconds // 3_600_000 | |
| milliseconds -= hours * 3_600_000 | |
| minutes = milliseconds // 60_000 | |
| milliseconds -= minutes * 60_000 | |
| seconds = milliseconds // 1_000 | |
| milliseconds -= seconds * 1_000 | |
| hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" | |
| return ( | |
| f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" | |
| ) | |
| def write_txt(transcript: Iterator[dict], file: TextIO): | |
| for segment in transcript: | |
| print(segment["text"].strip(), file=file, flush=True) | |
| def write_vtt(transcript: Iterator[dict], file: TextIO): | |
| print("WEBVTT\n", file=file) | |
| for segment in transcript: | |
| print( | |
| f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" | |
| f"{segment['text'].strip().replace('-->', '->')}\n", | |
| file=file, | |
| flush=True, | |
| ) | |
| def write_srt(transcript: Iterator[dict], file: TextIO): | |
| """ | |
| Write a transcript to a file in SRT format. | |
| Example usage: | |
| from pathlib import Path | |
| from whisper.utils import write_srt | |
| result = transcribe(model, audio_path, temperature=temperature, **args) | |
| # save SRT | |
| audio_basename = Path(audio_path).stem | |
| with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt: | |
| write_srt(result["segments"], file=srt) | |
| """ | |
| for i, segment in enumerate(transcript, start=1): | |
| # write srt lines | |
| print( | |
| f"{i}\n" | |
| f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " | |
| f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" | |
| f"{segment['text'].strip().replace('-->', '->')}\n", | |
| file=file, | |
| flush=True, | |
| ) | |