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Update app.py
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app.py
CHANGED
@@ -1,7 +1,7 @@
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import torch
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import gradio as gr
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import spaces
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import yt_dlp as youtube_dl
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from transformers import pipeline
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from transformers.pipelines.audio_utils import ffmpeg_read
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@@ -9,7 +9,7 @@ from transformers.pipelines.audio_utils import ffmpeg_read
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import tempfile
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import os
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MODEL_NAME = "
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BATCH_SIZE = 8
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FILE_LIMIT_MB = 1000
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YT_LENGTH_LIMIT_S = 3600 # limit to 1 hour YouTube files
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@@ -23,6 +23,7 @@ pipe = pipeline(
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device=device,
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)
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@spaces.GPU
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def transcribe(inputs, task):
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if inputs is None:
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@@ -71,7 +72,7 @@ def download_yt_audio(yt_url, filename):
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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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@@ -94,14 +95,13 @@ 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.Audio(type="filepath"),
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gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
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],
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outputs="text",
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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
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f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and π€ Transformers to transcribe audio files"
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" of arbitrary length."
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),
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@@ -111,14 +111,13 @@ mf_transcribe = gr.Interface(
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file_transcribe = gr.Interface(
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fn=transcribe,
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inputs=[
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gr.Audio(type="filepath", label="Audio file"),
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gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
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],
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outputs="text",
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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
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f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and π€ Transformers to transcribe audio files"
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" of arbitrary length."
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),
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@@ -132,10 +131,9 @@ yt_transcribe = gr.Interface(
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gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
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],
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outputs=["html", "text"],
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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
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f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and π€ Transformers to transcribe video files of"
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" arbitrary length."
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),
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import spaces
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import torch
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import gradio as gr
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import yt_dlp as youtube_dl
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from transformers import pipeline
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from transformers.pipelines.audio_utils import ffmpeg_read
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import tempfile
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import os
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MODEL_NAME = "ylacombe/whisper-large-v3-turbo"
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BATCH_SIZE = 8
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FILE_LIMIT_MB = 1000
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YT_LENGTH_LIMIT_S = 3600 # limit to 1 hour YouTube files
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device=device,
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)
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+
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@spaces.GPU
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def transcribe(inputs, task):
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if inputs is None:
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except youtube_dl.utils.ExtractorError as err:
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raise gr.Error(str(err))
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@spaces.GPU
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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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mf_transcribe = gr.Interface(
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fn=transcribe,
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inputs=[
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gr.Audio(sources="microphone", type="filepath"),
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gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
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],
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outputs="text",
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title="Whisper Large V3 Turbo: 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"
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f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and π€ Transformers to transcribe audio files"
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" of arbitrary length."
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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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gr.Audio(sources="upload", type="filepath", label="Audio file"),
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gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
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],
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outputs="text",
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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"
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f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and π€ Transformers to transcribe audio files"
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" of arbitrary length."
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),
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gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
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],
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outputs=["html", "text"],
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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 checkpoint"
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f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and π€ Transformers to transcribe video files of"
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" arbitrary length."
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),
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