实战whisper：本地化部署通用语音识别模型_java调用本地部署的语音识别模型whisper

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_writer

        2、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 )