import torch import gradio as gr from pytube import YouTube from transformers import pipeline MODEL_NAME = "linshoufan/linshoufan-whisper-small-nan-tw-pinyin-several-datasets" lang = "chinese" # 根據是否有可用的 CUDA 設備來選擇設備 device = 0 if torch.cuda.is_available() else "mps" # 初始化 pipeline,指定任務、模型和設備 pipe = pipeline( task="automatic-speech-recognition", chunk_length_s=15, model=MODEL_NAME, device=device, ) # 設置模型的語言和任務 pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe") # 定義轉錄功能 def transcribe(microphone=None, file_upload=None): warn_output = "" if microphone is not None and file_upload is not None: warn_output = "警告:您同時使用了麥克風與上傳音訊檔案,將只會使用麥克風錄製的檔案。\n" elif microphone is None and file_upload is None: return "錯誤:您必須至少使用麥克風或上傳一個音頻檔案。" file = microphone if microphone is not None else file_upload text = pipe(file)["text"] return warn_output + text # 定義 YouTube 轉寫功能 def yt_transcribe(yt_url): yt = YouTube(yt_url) stream = yt.streams.filter(only_audio=True).first() stream.download(filename="audio.mp3") text = pipe("audio.mp3")["text"] # 嵌入 YouTube 影片 video_id = yt_url.split("?v=")[-1] html_embed = f'
' return html_embed, text # 初始化 Gradio Blocks demo = gr.Blocks() # 定義兩個介面 mf_transcribe = gr.Interface( fn=transcribe, inputs=gr.Audio(label="audio",type="filepath"), outputs="text", title="Whisper 演示: 語音轉錄", description=f"演示使用 fine-tuned checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME} 以及 🤗 Transformers 轉錄任意長度的音訊檔案", allow_flagging="manual", ) yt_transcribe = gr.Interface( fn=yt_transcribe, inputs=[gr.Textbox(lines=1, placeholder="在此處貼上 YouTube 影片的 URL", label="YouTube URL")], outputs=["html", "text"], title="Whisper 演示: Youtube轉錄", description=f"演示使用 fine-tuned checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME} 以及 🤗 Transformers 轉錄任意長度的Youtube影片", allow_flagging="manual", ) # 將兩個介面加入到標籤介面中 with demo: gr.TabbedInterface([mf_transcribe, yt_transcribe], ["語音轉錄", "Youtube轉錄"]) # 啟動並分享 Gradio 介面 demo.launch(share=True)