Upload app.py

app.py

@@ -1,80 +1,99 @@
-import torch
-import gradio as gr
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
-
-
-
-# 加载模型和处理器
-def load_model(model_path, use_gpu=True, use_flash_attention_2=False, use_bettertransformer=False):
-    device = "cuda:0" if torch.cuda.is_available() and use_gpu else "cpu"
-    torch_dtype = torch.float16 if torch.cuda.is_available() and use_gpu else torch.float32
-
-    processor = AutoProcessor.from_pretrained(model_path)
-    model = AutoModelForSpeechSeq2Seq.from_pretrained(
-        model_path, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True,
-        use_flash_attention_2=use_flash_attention_2
-    )
-    if use_bettertransformer and not use_flash_attention_2:
-        model = model.to_bettertransformer()
-
-
-    model.to(device)
-
-    return processor, model, device, torch_dtype
-
-# 初始化模型
-processor, model, device, torch_dtype = load_model(
-    model_path=r"panlr/whisper-finetune-teochew",
-    use_gpu=True,
-    use_flash_attention_2=False,
-    use_bettertransformer=False
-)
-
-# 创建推理管道
-infer_pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    max_new_tokens=128,
-    chunk_length_s=30,
-    batch_size=16,
-    torch_dtype=torch_dtype,
-    device=device
-)
-
-# 定义推理函数
-def transcribe_audio(audio_path, num_beams=1):
-    generate_kwargs = {"num_beams": num_beams}
-
-    forced_decoder_ids = [
-        (1, processor.tokenizer.encode("<|startoftranscript|>")[0]),
-        (2, processor.tokenizer.encode("<|zh|>")[0]),
-        (3, processor.tokenizer.encode("<|transcribe|>")[0]),
-    ]
-    model.generation_config.forced_decoder_ids = forced_decoder_ids
-    # if language is not None:
-    #     generate_kwargs["language"] = language
-
-    result = infer_pipe(audio_path, return_timestamps=False, generate_kwargs=generate_kwargs)
-
-    return result['text']
-
-# Gradio 界面
-def gradio_interface(audio):
-    return transcribe_audio(audio)
-
-# 创建 Gradio 应用
-interface = gr.Interface(
-    fn=gradio_interface,
-    inputs=[
-        gr.Audio(sources=["microphone", "upload"], type="filepath", label="输入音频"),
-    ],
-    outputs=gr.Textbox(label="转录结果"),
-    title="Whisper 潮汕话-正字 语音转录",
-    description="上传音频文件或使用麦克风输入，进行语音转录。"
-)
-
-# 启动 Gradio 应用
-interface.launch()
-
+import torch
+import gradio as gr
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+
+
+# 加载模型和处理器
+def load_model(model_path, use_gpu=True, use_flash_attention_2=False, use_bettertransformer=False):
+    device = "cuda:0" if torch.cuda.is_available() and use_gpu else "cpu"
+    torch_dtype = torch.float16 if torch.cuda.is_available() and use_gpu else torch.float32
+
+    processor = AutoProcessor.from_pretrained(model_path)
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_path, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True,
+        use_flash_attention_2=use_flash_attention_2
+    )
+    if use_bettertransformer and not use_flash_attention_2:
+        model = model.to_bettertransformer()
+
+
+    model.to(device)
+
+    return processor, model, device, torch_dtype
+
+# 初始化模型
+processor, model, device, torch_dtype = load_model(
+    model_path=r"panlr/whisper-finetune-teochew",
+    use_gpu=True,
+    use_flash_attention_2=False,
+    use_bettertransformer=False
+)
+
+# 创建推理管道
+infer_pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    max_new_tokens=128,
+    chunk_length_s=30,
+    batch_size=16,
+    torch_dtype=torch_dtype,
+    device=device
+)
+
+# 定义推理函数
+def transcribe_audio(audio_path, num_beams=1):
+    generate_kwargs = {"num_beams": num_beams}
+
+    forced_decoder_ids = [
+        (1, processor.tokenizer.encode("<|startoftranscript|>")[0]),
+        (2, processor.tokenizer.encode("<|zh|>")[0]),
+        (3, processor.tokenizer.encode("<|transcribe|>")[0]),
+    ]
+    model.generation_config.forced_decoder_ids = forced_decoder_ids
+    # if language is not None:
+    #     generate_kwargs["language"] = language
+
+    result = infer_pipe(audio_path, return_timestamps=False, generate_kwargs=generate_kwargs)
+
+    return result['text']
+
+# Gradio 界面
+def gradio_interface(audio):
+    return transcribe_audio(audio)
+
+
+
+# 创建 Gradio 界面
+with gr.Blocks() as interface:
+    gr.Markdown("## Whisper 潮汕话-正字 语音转录")
+    
+    audio_input = gr.Audio(
+        sources=["microphone", "upload"], 
+        type="filepath", 
+        label="输入音频",
+        value="./example.wav"  # 指定默认音频文件
+    )
+    
+    output_text = gr.Textbox(label="转录结果")
+
+    # 在输入模块的下方添加说明
+    gr.Markdown("""
+        📢 **使用说明**
+        - 本demo部署在CPU上，所以推理速度较慢。对于比较书面的话语，识别效果还不错，对土话、俗话还需要更多的数据。
+        - 你可以 **上传音频文件** 或 **使用麦克风** 向模型输入。  
+        - 音频文件最好发音清晰、标准。  
+        - 默认提供一个示例音频，你可以直接点击“提交”查看转录效果。
+        - 示例音频的对应文本： 【状元 林大钦，兵部尚(siên7)书 翁万达，了佮 工部 左侍郎(se6 neng5) 陈一松，拢是 嘉靖 年间 介 进士】 
+    """)
+
+    submit_btn = gr.Button("提交")
+    submit_btn.click(gradio_interface, inputs=audio_input, outputs=output_text)
+
+# 启动 Gradio 应用
+interface.launch()
+
+ 
+