Update app.py
Browse files
app.py
CHANGED
@@ -31,47 +31,47 @@ def transcribe(inputs, task):
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return text
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def _return_yt_html_embed(yt_url):
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video_id = yt_url.split("?v=")[-1]
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HTML_str = (
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)
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return HTML_str
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def download_yt_audio(yt_url, filename):
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info_loader = youtube_dl.YoutubeDL()
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try:
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except youtube_dl.utils.DownloadError as err:
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file_length = info["duration_string"]
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file_h_m_s = file_length.split(":")
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file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
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if len(file_h_m_s) == 1:
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if len(file_h_m_s) == 2:
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file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
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if file_length_s > YT_LENGTH_LIMIT_S:
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ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
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with youtube_dl.YoutubeDL(ydl_opts) as ydl:
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def yt_transcribe(yt_url, task, max_filesize=75.0):
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#html_embed_str = _return_yt_html_embed(yt_url)
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#with tempfile.TemporaryDirectory() as tmpdirname:
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@@ -85,29 +85,11 @@ def yt_transcribe(yt_url, task, max_filesize=75.0):
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#text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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return None#html_embed_str, text
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demo = gr.Blocks()
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mf_transcribe = gr.Interface(
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fn=transcribe,
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inputs=[
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gr.inputs.Audio(source="microphone", type="filepath", optional=True),
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gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
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],
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outputs="text",
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layout="horizontal",
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theme="huggingface",
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title="Whisper Large V3: Transcribe Audio",
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description=(
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"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
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f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
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" of arbitrary length."
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),
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allow_flagging="never",
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)
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file_transcribe = gr.Interface(
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fn=transcribe,
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inputs=[
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@@ -126,26 +108,9 @@ file_transcribe = gr.Interface(
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allow_flagging="never",
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)
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yt_transcribe = gr.Interface(
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fn=yt_transcribe,
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inputs=[
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gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
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gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe")
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],
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outputs=["html", "text"],
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layout="horizontal",
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theme="huggingface",
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title="Whisper Large V3: Transcribe YouTube",
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description=(
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"Transcribe long-form YouTube videos with the click of a button! Demo uses the OpenAI Whisper checkpoint"
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f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
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" arbitrary length."
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),
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allow_flagging="never",
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)
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with demo:
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gr.TabbedInterface([
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demo.launch(enable_queue=True)
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return text
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#def _return_yt_html_embed(yt_url):
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#video_id = yt_url.split("?v=")[-1]
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#HTML_str = (
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# f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
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# " </center>"
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#)
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#return HTML_str
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#def download_yt_audio(yt_url, filename):
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#info_loader = youtube_dl.YoutubeDL()
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#
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#try:
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# info = info_loader.extract_info(yt_url, download=False)
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#except youtube_dl.utils.DownloadError as err:
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# raise gr.Error(str(err))
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#
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#file_length = info["duration_string"]
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#file_h_m_s = file_length.split(":")
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#file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
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#
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#if len(file_h_m_s) == 1:
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# file_h_m_s.insert(0, 0)
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#if len(file_h_m_s) == 2:
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# file_h_m_s.insert(0, 0)
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#file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
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#
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#if file_length_s > YT_LENGTH_LIMIT_S:
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# yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
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# file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
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# raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
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#
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#ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
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#
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#with youtube_dl.YoutubeDL(ydl_opts) as ydl:
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# try:
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# ydl.download([yt_url])
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# except youtube_dl.utils.ExtractorError as err:
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# raise gr.Error(str(err))
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#def yt_transcribe(yt_url, task, max_filesize=75.0):
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#html_embed_str = _return_yt_html_embed(yt_url)
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#with tempfile.TemporaryDirectory() as tmpdirname:
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#text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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#return None#html_embed_str, text
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demo = gr.Blocks()
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file_transcribe = gr.Interface(
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fn=transcribe,
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inputs=[
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allow_flagging="never",
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)
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with demo:
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gr.TabbedInterface([ file_transcribe], [ "Audio file"])
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demo.launch(enable_queue=True)
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