前言
Whisper 是一种通用语音识别模型。它是在大量不同音频数据集上进行训练的，也是一个多任务模型，可以执行多语言语音识别、语音翻译和语言识别。
这里呢，我将给出我的一些代码，来帮助你尽快实现【语音转文字】的服务部署。
以下是该AI模块的具体使用方式：
https://github.com/openai/whisper
心得
这是一个不错的语言模型，它支持自动识别语音语种，类似中文、英文、日语等它都能胜任，并且可以实现其他语种转英语翻译的功能，支持附加时间戳的字幕导出功能......
总体来说，它甚至可以与市面上领头的语言识别功能相媲美，并且主要它是开源的。
这是它的一些模型大小、需要的GPU显存、相对执行速度的对应表
这是它在命令行模式下的使用方式，这对想要尝尝鲜的小伙伴们来说，已经够了
tips：
1、首次安装完毕whisper后，执行指令时会给你安装你所选的模型，small、medium等，我的显卡已经不支持我使用medium了
2、关于GPU版本的pytorch，可以参考如下教程（使用CPU版本会比较慢）
https://blog.csdn.net/G541788_/article/details/135437236
python调用
作为一名python从业者，我十分幸运能够读懂一些模块的相关使用，这里我通过修改了一些模块源码调用，实现了在python代码中一键导出语音字幕的功能（这些功能在命令行中已拥有，但是我希望在使用python脚本model方法后再实现该功能，可能这些你并不需要，但随意吧）。
这个模块的cli()方法或许能更好实现这一功能（因为命令行模式，其实就是运行了这个方法，但我根据经验和实际代码来看，这会重复加载model，导致不必要的资源损耗）。
1、__init__.py中加入get_writer，让你能通过whisper模块去使用这个方法
from .transcribe import get_writer2、相关功能代码
import os.path import whisper import time # 这是语种langue参数的解释，或许对你的选择有帮助 LANGUAGES = { "en": "english", "zh": "chinese", "de": "german", "es": "spanish", "ru": "russian", "ko": "korean", "fr": "french", "ja": "japanese", "pt": "portuguese", "tr": "turkish", "pl": "polish", "ca": "catalan", "nl": "dutch", "ar": "arabic", "sv": "swedish", "it": "italian", "id": "indonesian", "hi": "hindi", "fi": "finnish", "vi": "vietnamese", "he": "hebrew", "uk": "ukrainian", "el": "greek", "ms": "malay", "cs": "czech", "ro": "romanian", "da": "danish", "hu": "hungarian", "ta": "tamil", "no": "norwegian", "th": "thai", "ur": "urdu", "hr": "croatian", "bg": "bulgarian", "lt": "lithuanian", "la": "latin", "mi": "maori", "ml": "malayalam", "cy": "welsh", "sk": "slovak", "te": "telugu", "fa": "persian", "lv": "latvian", "bn": "bengali", "sr": "serbian", "az": "azerbaijani", "sl": "slovenian", "kn": "kannada", "et": "estonian", "mk": "macedonian", "br": "breton", "eu": "basque", "is": "icelandic", "hy": "armenian", "ne": "nepali", "mn": "mongolian", "bs": "bosnian", "kk": "kazakh", "sq": "albanian", "sw": "swahili", "gl": "galician", "mr": "marathi", "pa": "punjabi", "si": "sinhala", "km": "khmer", "sn": "shona", "yo": "yoruba", "so": "somali", "af": "afrikaans", "oc": "occitan", "ka": "georgian", "be": "belarusian", "tg": "tajik", "sd": "sindhi", "gu": "gujarati", "am": "amharic", "yi": "yiddish", "lo": "lao", "uz": "uzbek", "fo": "faroese", "ht": "haitian creole", "ps": "pashto", "tk": "turkmen", "nn": "nynorsk", "mt": "maltese", "sa": "sanskrit", "lb": "luxembourgish", "my": "myanmar", "bo": "tibetan", "tl": "tagalog", "mg": "malagasy", "as": "assamese", "tt": "tatar", "haw": "hawaiian", "ln": "lingala", "ha": "hausa", "ba": "bashkir", "jw": "javanese", "su": "sundanese", "yue": "cantonese", } # 以下命令将使用medium模型转录音频文件中的语音： # # whisper audio.flac audio.mp3 audio.wav --model medium # 默认设置（选择模型small）非常适合转录英语。要转录包含非英语语音的音频文件，您可以使用以下选项指定语言--language： # # whisper japanese.wav --language Japanese # 添加--task translate会将演讲翻译成英语： # # whisper japanese.wav --language Japanese --task translate # 其他语言转录为英语 # whisper "E:\voice\恋愛サーキュレーション_(Vocals)_(Vocals).wav" --language ja --task translate # 这个任务是将audio_files内的声音文件进行字幕导出，以时间戳为单位存储到captions/目录里 audio_files = [r"E:\voice\恋愛サーキュレーション_(Vocals)_(Vocals).wav"] model = whisper.load_model("small") output_format = 'all' writer_args = { "highlight_words": False, "max_line_count": None, "max_line_width": None, "max_words_per_line": None, } for audio_file in audio_files: now_timestamp = str(int(time.time())) save_path = f'captions/{now_timestamp}' if not os.path.exists(save_path): os.mkdir(save_path) # language可选 # 中文zh,日语ja,英语en result = model.transcribe(audio_file, language='ja') writer = whisper.get_writer(output_format, save_path) writer(result, audio_file, **writer_args) print('done: ', audio_file )
