Add application file

README.md

@@ -1,12 +1,10 @@
 ---
-title: Whisper Taiwanese
-emoji: 📊
-colorFrom: blue
-colorTo: blue
+title: Whisper Small Taiwanese Lora
+emoji: 🎤
+colorFrom: indigo
+colorTo: pink
 sdk: gradio
-sdk_version: 4.44.1
+sdk_version: 4.29.0
 app_file: app.py
 pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---

app.py

@@ -0,0 +1,176 @@
+import spaces
+import torch
+import gradio as gr
+import os
+from peft import PeftModel, PeftConfig
+
+import tempfile
+from transformers import (
+    pipeline,
+    WhisperForConditionalGeneration,
+    WhisperTokenizer,
+    WhisperProcessor,
+)
+from yt_dlp import YoutubeDL
+
+# 確認支援的運算設備
+device = "cuda"
+print(f"Using device: {device}")
+
+# 參數設定
+yt_length_limit_s = 600  # 限制 YouTube 影片的最大長度為 10 分鐘
+peft_model_id = "yuweiiizz/whisper-small-taiwanese-lora"
+language = "Chinese"
+task = "transcribe"
+
+# 讀取 cookies 內容
+cookies_content = os.getenv('COOKIES')
+
+# 如果 cookies 存在，寫入暫存檔案
+if cookies_content:
+    cookies_temp_path = os.path.join(tempfile.gettempdir(), 'cookies.txt')
+    with open(cookies_temp_path, 'w') as f:
+        f.write(cookies_content)
+else:
+    raise ValueError("COOKIES secret is not set.")
+
+# 模型與處理器初始化
+peft_config = PeftConfig.from_pretrained(peft_model_id)
+model = WhisperForConditionalGeneration.from_pretrained(peft_config.base_model_name_or_path, device_map=device)
+model = PeftModel.from_pretrained(model, peft_model_id)
+tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
+processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
+feature_extractor = processor.feature_extractor
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model, 
+    tokenizer=tokenizer, 
+    feature_extractor=feature_extractor,
+    max_new_tokens=128,
+    chunk_length_s=15
+    )
+
+# 轉錄音訊檔案的功能
+@spaces.GPU
+def transcribe(microphone=None, file_upload=None):
+    if microphone and file_upload:
+        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"
+        file = microphone
+    elif microphone or file_upload:
+        warn_output = ""
+        file = microphone if microphone else file_upload
+    else:
+        return "錯誤：您必須至少使用麥克風或上傳一個音訊檔案。"
+
+    text = pipe(file, generate_kwargs={"task": task, "language": language})["text"]
+    return warn_output + text
+
+# 轉錄 YouTube 影片的功能
+@spaces.GPU
+def yt_transcribe(yt_url):
+    try:
+        import stable_whisper
+
+        # 使用 yt-dlp 下載音訊
+        ydl_opts = {
+            'format': 'bestaudio/best',
+            'noplaylist': True,
+            'quiet': True,
+            'cookiefile' : cookies_temp_path,
+            'outtmpl': os.path.join(tempfile.gettempdir(), '%(id)s.%(ext)s'),
+            'postprocessors': [{
+                'key': 'FFmpegExtractAudio',
+                'preferredcodec': 'mp3',
+                'preferredquality': '192',
+            }],
+        }
+        with YoutubeDL(ydl_opts) as ydl:
+            info_dict = ydl.extract_info(yt_url, download=True)
+            audio_path = ydl.prepare_filename(info_dict)
+            audio_path = os.path.splitext(audio_path)[0] + ".mp3"
+
+        # 定義轉錄函數
+        def inference(audio, **kwargs) -> dict:
+            pipe_output = pipe(audio, generate_kwargs={"task": task, "language": language}, return_timestamps=True)
+            chunks = [{"start": c['timestamp'][0] or 0, "end": c['timestamp'][1] or c['timestamp'][0] + 5, "text": c['text']} for c in pipe_output['chunks']]
+            return chunks
+
+        # 使用 stable_whisper 進行轉錄
+        result = stable_whisper.transcribe_any(inference, audio_path, vad=True)
+        os.remove(audio_path)
+        
+        # 解析 URL 中的 video ID
+        video_id = info_dict.get('id', None)
+        if not video_id:
+            return "錯誤：無法解析 YouTube 影片 ID。", "", None
+        
+        # 嵌入 YouTube 影片
+        html_embed = f'<center><iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"></iframe></center>'
+        
+        # 格式化字幕並保存為 SRT 檔案
+        srt_text = result.to_srt_vtt(word_level=False)
+        srt_path = os.path.join(tempfile.gettempdir(), f"{video_id}.srt")
+        with open(srt_path, 'w') as srt_file:
+            srt_file.write(srt_text)
+        
+        return html_embed, srt_text, srt_path
+    except Exception as e:
+        return f"錯誤：處理 YouTube 影片時發生錯誤。錯誤詳情：{str(e)}", "", None
+
+# 範例音訊檔案
+example_paths = [
+    ["examples/common_voice_1.mp3"],
+    ["examples/common_voice_2.mp3"],
+    ["examples/common_voice_3.mp3"],
+    ["examples/dictionary_1.mp3"],
+    ["examples/dictionary_2.mp3"],
+    ["examples/dictionary_3.mp3"],
+]
+
+example_info = """
+| Example File | 台語漢字 | 華語 | 拼音 |
+|--------------|------------|------|------|
+| common_voice_1.mp3 | 我欲學臺語 | 我要學臺語 | guá beh o̍h Tâi-gí |
+| common_voice_2.mp3 | 有這款的代誌,我攏毋知 | 有這種事情,我都不知道 | ū tsit-khuán ê tāi-tsì, guá lóng m̄ tsai |
+| common_voice_3.mp3 | 豐原 | 豐原 | Hong-guân |
+| dictionary_1.mp3 | 你今仔日下晡去佗愛交代清楚。 | 你今天下午到哪去要說明清楚。 | Lí kin-á-ji̍t ē-poo khì toh ài kau-tài tshing-tshó. |
+| dictionary_2.mp3 | 𠢕眩船的人愛食眩船藥仔。 | 容易暈船的人要吃暈船藥。 | Gâu hîn-tsûn ê lâng ài tsia̍h hîn-tsûn io̍h-á |
+| dictionary_3.mp3 | 三分天註定，七分靠拍拚。 | 三分天注定，七分靠努力。 | Sann hun thinn tsù-tiānn, tshit hun khò phah-piànn. |
+"""
+
+# 獲取範例檔案的絕對路徑
+script_dir = os.path.dirname(os.path.abspath(__file__))
+examples = [os.path.join(script_dir, example_path[0]) for example_path in example_paths]
+
+# Gradio 介面
+demo = gr.Blocks()
+
+mf_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(label="audio", type="filepath"),
+    outputs="text",
+    title="Whisper 台語演示: 語音轉錄",
+    description=f"演示使用 `PEFT-LoRA` fine-tuned checkpoint [{peft_model_id}](https://huggingface.co/{peft_model_id} 轉錄任意長度的音訊檔案",
+    allow_flagging="manual",
+    examples=examples,
+    article=example_info,
+)
+
+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=[
+        gr.HTML(label="YouTube Video Embed"),
+        gr.Textbox(label="轉錄稿"),
+        gr.File(label="下載 SRT 檔案")
+    ],
+    title="Whisper 台語演示: Youtube轉錄",
+    description=f"演示使用 `PEFT-LoRA` fine-tuned checkpoint [{peft_model_id}](https://huggingface.co/{peft_model_id} 轉錄任意長度的Youtube影片",
+    allow_flagging="manual",
+)
+
+with demo:
+    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["語音轉錄", "Youtube轉錄"])
+
+demo.launch(share=True)

requirements.txt

@@ -0,0 +1,17 @@
+accelerate==0.27.2
+datasets==2.18.0
+evaluate==0.4.1
+gradio==4.20.1
+jiwer==3.0.3
+librosa==0.10.1
+moviepy==1.0.3
+peft==0.10.0
+pysrt==1.1.2
+soundfile==0.12.1
+stable-ts==2.17.3
+streamlit==1.33.0
+tensorboard==2.16.2
+transformers==4.38.2
+torch==2.1.2
+watchdog==4.0.0
+yt-dlp==2023.11.16