Update app.py

app.py

@@ -1,201 +1,24 @@
 
 
 
-# import spaces
-# import torch
-
-# import gradio as gr
-# import yt_dlp as youtube_dl
-# from pytubefix import YouTube
-# from pytubefix.cli import on_progress
-
-# from transformers import pipeline
-# from transformers.pipelines.audio_utils import ffmpeg_read
-
-# import tempfile
-# import os
-
-# MODEL_NAME = "razhan/whisper-base-hawrami-transcription"
-# BATCH_SIZE = 1
-# FILE_LIMIT_MB = 10
-# YT_LENGTH_LIMIT_S = 60 * 10  # 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,
-# )
-
-
-# # @spaces.GPU
-# def transcribe(inputs, task="transcribe"):
-#     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))
-#     # yt = pt.YouTube(yt_url)
-#     # stream = yt.streams.filter(only_audio=True)[0]
-#     # stream.download(filename=filename)
-
-# # @spaces.GPU
-# # def yt_transcribe(yt_url, task="transcribe", 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")
-# #         filepath = os.path.join(tmpdirname, "audio.mp3")
-# #         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
-
-
-# def yt_transcribe(yt_url, task="transcribe", progress=gr.Progress(), max_filesize=75.0):
-#     progress(0, desc="Loading audio file...")
-#     html_embed_str = _return_yt_html_embed(yt_url)
-#     try:
-#         # yt = pytube.YouTube(yt_url)
-#         # stream = yt.streams.filter(only_audio=True)[0]
-#         yt = YouTube(yt_url, on_progress_callback = on_progress, use_po_token=True)
-         
-#         stream = yt.streams.get_audio_only()
-        
-#     except:
-#         raise gr.Error("An error occurred while loading the YouTube video. Please try again.")
-
-#     if stream.filesize_mb > max_filesize:
-#         raise gr.Error(f"Maximum YouTube file size is {max_filesize}MB, got {stream.filesize_mb:.2f}MB.")
-
-#     # stream.download(filename="audio.mp3")
-#     stream.download(filename="audio.mp3", mp3=True)
-
-#     with open("audio.mp3", "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(theme=gr.themes.Ocean())
-
-# mf_transcribe = gr.Interface(
-#     fn=transcribe,
-#     inputs=[
-#         gr.Audio(sources="microphone", type="filepath"),
-#         # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
-#     ],
-#     outputs="text",
-#     title="Whisper Horami Demo: 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."
-#     ),
-#     flagging_mode="never",
-# )
-
-# file_transcribe = gr.Interface(
-#     fn=transcribe,
-#     inputs=[
-#         gr.Audio(sources="upload", type="filepath", label="Audio file"),
-#         # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
-#     ],
-#     outputs="text",
-#     title="Whisper Horami Demo: 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."
-#     ),
-#     flagging_mode="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")
-#     ],
-#     outputs=["html", "text"],
-#     title="Whisper Horami Demo: Translate 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."
-#     ),
-#     flagging_mode="never",
-# )
-
-# with demo:
-#     # gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
-#     gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
-
-# demo.queue().launch(ssr_mode=False)
-
 import spaces
 import torch
+
 import gradio as gr
+import yt_dlp as youtube_dl
 from pytubefix import YouTube
 from pytubefix.cli import on_progress
+
 from transformers import pipeline
 from transformers.pipelines.audio_utils import ffmpeg_read
+
 import tempfile
 import os
 
 MODEL_NAME = "razhan/whisper-base-hawrami-transcription"
 BATCH_SIZE = 1
+FILE_LIMIT_MB = 10
+YT_LENGTH_LIMIT_S = 60 * 10  # limit to 1 hour YouTube files
 
 device = 0 if torch.cuda.is_available() else "cpu"
 
@@ -206,83 +29,260 @@ pipe = pipeline(
     device=device,
 )
 
+
+# @spaces.GPU
 def transcribe(inputs, task="transcribe"):
     if inputs is None:
-        raise gr.Error("Please upload or record an audio file before submitting.")
+        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
 
-    result = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
-    return result["text"]
 
 def _return_yt_html_embed(yt_url):
     video_id = yt_url.split("?v=")[-1]
-    return f'<center><iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"></iframe></center>'
+    HTML_str = (
+        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
+        " </center>"
+    )
+    return HTML_str
 
-def yt_transcribe(yt_url, task="transcribe", progress=gr.Progress()):
-    progress(0, desc="Loading audio file...")
-    html_embed = _return_yt_html_embed(yt_url)
+# 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))
+    # yt = pt.YouTube(yt_url)
+    # stream = yt.streams.filter(only_audio=True)[0]
+    # stream.download(filename=filename)
+
+# @spaces.GPU
+# def yt_transcribe(yt_url, task="transcribe", 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")
+#         filepath = os.path.join(tmpdirname, "audio.mp3")
+#         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
+
+
+def yt_transcribe(yt_url, task="transcribe", progress=gr.Progress(), max_filesize=75.0):
+    progress(0, desc="Loading audio file...")
+    html_embed_str = _return_yt_html_embed(yt_url)
     try:
-        yt = YouTube(yt_url, on_progress_callback=on_progress, use_po_token=True)
+        # yt = pytube.YouTube(yt_url)
+        # stream = yt.streams.filter(only_audio=True)[0]
+        yt = YouTube(yt_url, on_progress_callback = on_progress, use_po_token=True)
+         
         stream = yt.streams.get_audio_only()
-    except Exception as e:
-        raise gr.Error(f"Error loading YouTube video: {str(e)}")
-
-    with tempfile.TemporaryDirectory() as tmpdir:
-        file_path = os.path.join(tmpdir, "audio.mp3")
-        stream.download(filename=file_path)
         
-        with open(file_path, "rb") as f:
-            audio_data = f.read()
+    except:
+        raise gr.Error("An error occurred while loading the YouTube video. Please try again.")
 
-    audio = ffmpeg_read(audio_data, pipe.feature_extractor.sampling_rate)
-    inputs = {"array": audio, "sampling_rate": pipe.feature_extractor.sampling_rate}
-    
-    result = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
-    return html_embed, result["text"]
+    if stream.filesize_mb > max_filesize:
+        raise gr.Error(f"Maximum YouTube file size is {max_filesize}MB, got {stream.filesize_mb:.2f}MB.")
 
-demo = gr.Blocks(theme=gr.themes.Ocean())
+    # stream.download(filename="audio.mp3")
+    stream.download(filename="audio.mp3", mp3=True)
+
+    with open("audio.mp3", "rb") as f:
+        inputs = f.read()
 
-common_inputs = [
-    gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
-]
+    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(theme=gr.themes.Ocean())
 
 mf_transcribe = gr.Interface(
     fn=transcribe,
     inputs=[
         gr.Audio(sources="microphone", type="filepath"),
-        *common_inputs
+        # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
     ],
     outputs="text",
-    title="Whisper Horami: Live Transcription",
-    description="Transcribe audio from your microphone in real-time"
+    title="Whisper Horami Demo: 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."
+    ),
+    flagging_mode="never",
 )
 
 file_transcribe = gr.Interface(
     fn=transcribe,
     inputs=[
         gr.Audio(sources="upload", type="filepath", label="Audio file"),
-        *common_inputs
+        # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
     ],
     outputs="text",
-    title="Whisper Horami: File Transcription",
-    description="Upload an audio file for transcription"
+    title="Whisper Horami Demo: 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."
+    ),
+    flagging_mode="never",
 )
 
-yt_interface = gr.Interface(
+yt_transcribe = gr.Interface(
     fn=yt_transcribe,
     inputs=[
-        gr.Textbox(placeholder="YouTube URL", label="Video URL"),
-        *common_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")
     ],
     outputs=["html", "text"],
-    title="Whisper Horami: YouTube Transcription",
-    description="Transcribe audio from YouTube videos"
+    title="Whisper Horami Demo: Translate 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."
+    ),
+    flagging_mode="never",
 )
 
 with demo:
-    gr.TabbedInterface(
-        [mf_transcribe, file_transcribe],
-        ["Microphone", "Audio File",]
-    )
+    # gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
+    gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
 
 demo.queue().launch(ssr_mode=False)
+
+# import spaces
+# import torch
+# import gradio as gr
+# from pytubefix import YouTube
+# from pytubefix.cli import on_progress
+# from transformers import pipeline
+# from transformers.pipelines.audio_utils import ffmpeg_read
+# import tempfile
+# import os
+
+# MODEL_NAME = "razhan/whisper-base-hawrami-transcription"
+# BATCH_SIZE = 1
+
+# 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="transcribe"):
+#     if inputs is None:
+#         raise gr.Error("Please upload or record an audio file before submitting.")
+
+#     result = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
+#     return result["text"]
+
+# def _return_yt_html_embed(yt_url):
+#     video_id = yt_url.split("?v=")[-1]
+#     return f'<center><iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"></iframe></center>'
+
+# def yt_transcribe(yt_url, task="transcribe", progress=gr.Progress()):
+#     progress(0, desc="Loading audio file...")
+#     html_embed = _return_yt_html_embed(yt_url)
+    
+#     try:
+#         yt = YouTube(yt_url, on_progress_callback=on_progress, use_po_token=True)
+#         stream = yt.streams.get_audio_only()
+#     except Exception as e:
+#         raise gr.Error(f"Error loading YouTube video: {str(e)}")
+
+#     with tempfile.TemporaryDirectory() as tmpdir:
+#         file_path = os.path.join(tmpdir, "audio.mp3")
+#         stream.download(filename=file_path)
+        
+#         with open(file_path, "rb") as f:
+#             audio_data = f.read()
+
+#     audio = ffmpeg_read(audio_data, pipe.feature_extractor.sampling_rate)
+#     inputs = {"array": audio, "sampling_rate": pipe.feature_extractor.sampling_rate}
+    
+#     result = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
+#     return html_embed, result["text"]
+
+# demo = gr.Blocks(theme=gr.themes.Ocean())
+
+# common_inputs = [
+#     gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
+# ]
+
+# mf_transcribe = gr.Interface(
+#     fn=transcribe,
+#     inputs=[
+#         gr.Audio(sources="microphone", type="filepath"),
+#         *common_inputs
+#     ],
+#     outputs="text",
+#     title="Whisper Horami: Live Transcription",
+#     description="Transcribe audio from your microphone in real-time"
+# )
+
+# file_transcribe = gr.Interface(
+#     fn=transcribe,
+#     inputs=[
+#         gr.Audio(sources="upload", type="filepath", label="Audio file"),
+#         *common_inputs
+#     ],
+#     outputs="text",
+#     title="Whisper Horami: File Transcription",
+#     description="Upload an audio file for transcription"
+# )
+
+# yt_interface = gr.Interface(
+#     fn=yt_transcribe,
+#     inputs=[
+#         gr.Textbox(placeholder="YouTube URL", label="Video URL"),
+#         *common_inputs
+#     ],
+#     outputs=["html", "text"],
+#     title="Whisper Horami: YouTube Transcription",
+#     description="Transcribe audio from YouTube videos"
+# )
+
+# with demo:
+#     gr.TabbedInterface(
+#         [mf_transcribe, file_transcribe],
+#         ["Microphone", "Audio File",]
+#     )
+
+# demo.queue().launch(ssr_mode=False)