Update app.py

app.py

@@ -2,12 +2,6 @@
 
 
 
-
-def install(package):
-    if hasattr(pip, 'main'):
-        pip.main(['install', package])
-install("speechbrain")
-
 import torch
 
 import gradio as gr
@@ -29,95 +23,6 @@ model = separator.from_hparams(source="speechbrain/sepformer-libri2mix", savedir
 
 
 
-
-
-
-
-
-
-
-# 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_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 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"}
-    
-# #     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, "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}
-
-# #     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-
-# #     return html_embed_str, text
-
-
 demo = gr.Blocks()
 
 
@@ -141,62 +46,4 @@ with demo:
 
 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)