Update app.py

app.py

@@ -1,20 +1,27 @@
+
+
+
+# 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 huggingface_hub import model_info
-# import re
+# from transformers.pipelines.audio_utils import ffmpeg_read
+
 # import tempfile
 # import os
 
-# MODEL_NAME = "razhan/whisper-small-ckb"
+# MODEL_NAME = "razhan/whisper-base-hawrami-transcription"
 # BATCH_SIZE = 1
 # FILE_LIMIT_MB = 10
-# YT_LENGTH_LIMIT_S = 60 * 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,
@@ -22,164 +29,173 @@
 #     device=device,
 # )
 
-# pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(task="transcribe")
-
-# def transcribe(microphone, file_upload):
-#     warn_output = ""
-#     if (microphone is not None) and (file_upload is not None):
-#         warn_output = (
-#             "WARNING: You've uploaded an audio file and used the microphone. "
-#             "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
-#         )
 
-#     elif (microphone is None) and (file_upload is None):
-#         return "ERROR: You have to either use the microphone or upload an audio file"
+# # @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.")
 
-#     file = microphone if microphone is not None else file_upload
-
-#     text = pipe(file)["text"]
-
-#     return warn_output + text
+#     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+#     return  text
 
 
 # def _return_yt_html_embed(yt_url):
-#     if 'youtu.be' in yt_url:
-#         video_id = yt_url.split('/')[-1].split('?')[0]
-#     else:
-#         video_id = yt_url.split("?v=")[-1].split('&')[0]
-
+#     video_id = yt_url.split("?v=")[-1]
 #     HTML_str = (
-#         f'<center><iframe width="560" height="315" src="https://www.youtube.com/embed/{video_id}" '
-#         'frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" '
-#         'allowfullscreen></iframe></center>'
+#         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}
 
-# def yt_transcribe(yt_url, task="transcribe", max_filesize=75.0, progress=gr.Progress()):
-#     html_embed_str = _return_yt_html_embed(yt_url)
+# #     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
 
-#     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()
+# #     return html_embed_str, text
 
-#     inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
-#     inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
 
+# 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.")
 
-#     start_time = time.time()
-#     outputs = pipe(inputs, chunk_length_s=30, batch_size=BATCH_SIZE, generate_kwargs={"task": task, "language": "persian"}, return_timestamps=False)
-#     exec_time = time.time() - start_time
-#     logging.info(print(f"transcribe: {exec_time} sec."))
-    
-#     return html_embed_str,  txt, exec_time
+#     # stream.download(filename="audio.mp3")
+#     stream.download(filename="audio.mp3", mp3=True)
 
+#     with open("audio.mp3", "rb") as f:
+#         inputs = f.read()
 
-# def download_yt_audio(yt_url, filename, progress=gr.Progress()):
-#     if '&list' in yt_url:
-#         yt_url = yt_url.split('&list')[0]
-         
-#     info_loader = youtube_dl.YoutubeDL()
+#     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
 
-#     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"}
-#     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))
-#     progress(1, desc="Video downloaded from YouTube!")
 
+# demo = gr.Blocks(theme=gr.themes.Ocean())
 
 # mf_transcribe = gr.Interface(
 #     fn=transcribe,
 #     inputs=[
 #         gr.Audio(sources="microphone", type="filepath"),
-#         gr.Audio(sources="upload", type="filepath"),
+#         # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
 #     ],
 #     outputs="text",
-#     title="Whisper Central Kurdish‌ (Sorani) Demo: Transcribe Audio",
+#     title="Whisper Horami Demo: Transcribe Audio",
 #     description=(
-#         "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned"
+#         "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",
+#     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")],
-#     outputs=["html",
-#         gr.Textbox(
-#                 label="Output",
-#                 rtl=True,
-#                 show_copy_button=True,
-#         ),
-#         gr.Text(label="Transcription Time")
-#         ],
-#     title="Whisper Central Kurdish‌ (Sorani) Demo: Transcribe YouTube",
+#     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 the fine-tuned checkpoint:"
-#         f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of"
+#         "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",
+#     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 = gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
-
-# if __name__ == "__main__":
-#     demo.launch()
-
-
+# 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"
 
@@ -190,157 +206,83 @@ pipe = pipeline(
     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
+        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]
-    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
-
+    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(), max_filesize=75.0):
+def yt_transcribe(yt_url, task="transcribe", progress=gr.Progress()):
     progress(0, desc="Loading audio file...")
-    html_embed_str = _return_yt_html_embed(yt_url)
+    html_embed = _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)
-         
+        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)
+    except Exception as e:
+        raise gr.Error(f"Error loading YouTube video: {str(e)}")
 
-    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
+    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"),
-        # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+        *common_inputs
     ],
     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",
+    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"),
-        # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+        *common_inputs
     ],
     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",
+    title="Whisper Horami: File Transcription",
+    description="Upload an audio file for transcription"
 )
 
-yt_transcribe = gr.Interface(
+yt_interface = 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")
+        gr.Textbox(placeholder="YouTube URL", label="Video URL"),
+        *common_inputs
     ],
     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",
+    title="Whisper Horami: YouTube Transcription",
+    description="Transcribe audio from YouTube videos"
 )
 
 with demo:
-    # gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
-    gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
+    gr.TabbedInterface(
+        [mf_transcribe, file_transcribe],
+        ["Microphone", "Audio File",]
+    )
 
 demo.queue().launch(ssr_mode=False)