Update app.py

app.py

@@ -1,137 +1,60 @@
-import torch
-import gradio as gr
-import yt-dlp as youtube_dl
-from transformers import pipeline
-from transformers.pipelines.audio_utils import ffmpeg_read
-
-import tempfile
 import os
 import time
-
-MODEL_NAME = "openai/whisper-large-v3"
-BATCH_SIZE = 8
-FILE_LIMIT_MB = 1000
-YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
-
-device = 0 if torch.cuda.is_available() else "cpu"
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-)
-
-def transcribe(inputs, task):
-    if inputs is None:
-        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
-
-    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-    return text
-
-def _return_yt_html_embed(yt_url):
-    video_id = yt_url.split("?v=")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
-
-def download_yt_audio(yt_url, filename):
-    info_loader = youtube_dl.YoutubeDL()
-    
-    try:
-        info = info_loader.extract_info(yt_url, download=False)
-    except youtube_dl.utils.DownloadError as err:
-        raise gr.Error(str(err))
-    
-    file_length = info["duration"]
-    file_length_s = int(file_length)
-    
-    if file_length_s > YT_LENGTH_LIMIT_S:
-        yt_length_limit_hms = time.strftime("%H:%M:%S", time.gmtime(YT_LENGTH_LIMIT_S))
-        file_length_hms = time.strftime("%H:%M:%S", time.gmtime(file_length_s))
-        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
-    
-    ydl_opts = {"outtmpl": filename, "format": "bestaudio/best"}
-    
-    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
-        try:
-            ydl.download([yt_url])
-        except youtube_dl.utils.ExtractorError as err:
-            raise gr.Error(str(err))
-
-def yt_transcribe(yt_url, task, max_filesize=75.0):
-    html_embed_str = _return_yt_html_embed(yt_url)
-
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        filepath = os.path.join(tmpdirname, "audio.m4a")
-        download_yt_audio(yt_url, filepath)
-        with open(filepath, "rb") as f:
-            inputs = f.read()
-
-    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
-    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
-
-    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-
-    return html_embed_str, text
-
-mf_transcribe = gr.Interface(
+import json
+import gradio as gr
+import torch
+import torchaudio
+import numpy as np
+from denoiser.demucs import Demucs
+from pydub import AudioSegment
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# 設置 Hugging Face Hub 的 Access Token
+auth_token = os.getenv("HF_TOKEN")
+
+# 加載私有模型
+model_id = "DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser"
+model = AutoModelForSequenceClassification.from_pretrained(model_id, token=auth_token)
+tokenizer = AutoTokenizer.from_pretrained(model_id, token=auth_token)
+
+def transcribe(file_upload, microphone):
+    file = microphone if microphone is not None else file_upload
+    demucs_model = Demucs(hidden=64)
+    state_dict = torch.load("path_to_model_checkpoint", map_location='cpu')  # 請確保提供正確的模型文件路徑
+    demucs_model.load_state_dict(state_dict)
+    x, sr = torchaudio.load(file)
+    out = demucs_model(x[None])[0]
+    out = out / max(out.abs().max().item(), 1)
+    torchaudio.save('enhanced.wav', out, sr)
+    enhanced = AudioSegment.from_wav('enhanced.wav')  # 只有去完噪的需要降bitrate再做語音識別
+    enhanced.export('enhanced.wav', format="wav", bitrate="256k")
+
+    # 假設模型是用於文本分類
+    inputs = tokenizer("enhanced.wav", return_tensors="pt")
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+    return "enhanced.wav", predictions
+
+demo = gr.Interface(
     fn=transcribe,
     inputs=[
-        gr.Audio(type="filepath"),
-        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+        gr.Audio(type="filepath", label="語音質檢麥克風實時錄音"),
+        gr.Audio(type="filepath", label="語音質檢原始音檔"),
     ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V3: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-file_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.Audio(type="filepath", label="Audio file"),
-        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+    outputs=[
+        gr.Audio(type="filepath", label="Output"),
+        gr.Textbox(label="Model Predictions")
     ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V3: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
+    title="<p style='text-align: center'><a href='https://www.twman.org/AI' target='_blank'>語音質檢噪音去除 (語音增強)：Meta Denoiser</a>",
+    description="為了提升語音識別的效果，可以在識別前先進行噪音去除",
     allow_flagging="never",
-)
-
-yt_transcribe = gr.Interface(
-    fn=yt_transcribe,
-    inputs=[
-        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
+    examples=[
+        ["exampleAudio/15s_2020-03-27_sep1.wav"],
+        ["exampleAudio/13s_2020-03-27_sep2.wav"],
+        ["exampleAudio/30s_2020-04-23_sep1.wav"],
+        ["exampleAudio/15s_2020-04-23_sep2.wav"],
     ],
-    outputs=["html", "text"],
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V3: Transcribe YouTube",
-    description=(
-        "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
-        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
-        " arbitrary length."
-    ),
-    allow_flagging="never",
 )
 
-with gr.Blocks() as demo:
-    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
-
-demo.launch(enable_queue=True)
+demo.launch(debug=True)