Update app.py

app.py

@@ -1,3 +1,10 @@
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 import torch
 
 import gradio as gr
@@ -8,144 +15,183 @@ from transformers.pipelines.audio_utils import ffmpeg_read
 import tempfile
 import os
 
-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,
-)
 
+model = separator.from_hparams(source="speechbrain/sepformer-libri2mix", savedir='pretrained_models/sepformer-libri2mix')
 
-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()
+
+
+
+
+
+
+
+# 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))
+# #     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_string"]
-    file_h_m_s = file_length.split(":")
-    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
+# #     file_length = info["duration_string"]
+# #     file_h_m_s = file_length.split(":")
+# #     file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
     
-    if len(file_h_m_s) == 1:
-        file_h_m_s.insert(0, 0)
-    if len(file_h_m_s) == 2:
-        file_h_m_s.insert(0, 0)
-    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
+# #     if len(file_h_m_s) == 1:
+# #         file_h_m_s.insert(0, 0)
+# #     if len(file_h_m_s) == 2:
+# #         file_h_m_s.insert(0, 0)
+# #     file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
     
-    if file_length_s > YT_LENGTH_LIMIT_S:
-        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
-        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
-        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
+# #     if file_length_s > YT_LENGTH_LIMIT_S:
+# #         yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
+# #         file_length_hms = time.strftime("%HH:%MM:%SS", 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": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
+# #     ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/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))
+# #     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)
+# # 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, "video.mp4")
-        download_yt_audio(yt_url, filepath)
-        with open(filepath, "rb") as f:
-            inputs = f.read()
+# #     with tempfile.TemporaryDirectory() as tmpdirname:
+# #         filepath = os.path.join(tmpdirname, "video.mp4")
+# #         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}
+# #     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"]
+# #     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
 
-    return html_embed_str, text
+# #     return html_embed_str, text
 
 
 demo = gr.Blocks()
 
-mf_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-    ],
-    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 OpenAI Whisper"
-        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.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-    ],
-    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 OpenAI Whisper"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-)
+def separateaudio(filepath):
+    est_sources = model.separate_file(path=filepath) 
+    torchaudio.save("file.wav", est_sources[:, :, 0].detach().cpu(), 8000)
+    
 
-yt_transcribe = gr.Interface(
-    fn=yt_transcribe,
-    inputs=[
-        gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe")
-    ],
-    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 OpenAI Whisper checkpoint"
-        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
-        " arbitrary length."
-    ),
-    allow_flagging="never",
+separation = gr.Interface(
+    fn=transcribe_speech,
+    inputs=gr.Audio(sources="upload", type="filepath"),
+    outputs=gr.outputs.Textbox(),
 )
 
+
 with demo:
-    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
+    gr.TabbedInterface(
+        [separation],
+        ["Separate audio file"],
+    )
+
+
 
-demo.launch(enable_queue=True)
+# mf_transcribe = gr.Interface(
+#     fn=transcribe,
+#     inputs=[
+#         gr.inputs.Audio(source="microphone", type="filepath", optional=True),
+#         gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
+#     ],
+#     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 OpenAI Whisper"
+#         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.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
+#         gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
+#     ],
+#     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 OpenAI Whisper"
+#         f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
+#         " of arbitrary length."
+#     ),
+#     allow_flagging="never",
+# )
+
+# yt_transcribe = gr.Interface(
+#     fn=yt_transcribe,
+#     inputs=[
+#         gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
+#         gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe")
+#     ],
+#     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 OpenAI Whisper checkpoint"
+#         f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
+#         " arbitrary length."
+#     ),
+#     allow_flagging="never",
+# )
+
+# with demo:
+#     gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
+
+# demo.launch(enable_queue=True)