Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -1,11 +1,9 @@
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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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-
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import tempfile
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import os
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@@ -29,8 +27,7 @@ def transcribe(inputs, task):
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raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
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text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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return
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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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@@ -63,7 +60,14 @@ def download_yt_audio(yt_url, filename):
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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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ydl_opts = {
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with youtube_dl.YoutubeDL(ydl_opts) as ydl:
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try:
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@@ -71,12 +75,11 @@ 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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with tempfile.TemporaryDirectory() as tmpdirname:
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filepath = os.path.join(tmpdirname, "
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download_yt_audio(yt_url, filepath)
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with open(filepath, "rb") as f:
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inputs = f.read()
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@@ -86,8 +89,7 @@ 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 html_embed_str, text
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demo = gr.Blocks()
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@@ -131,7 +133,11 @@ yt_transcribe = gr.Interface(
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gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
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gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
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],
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outputs=[
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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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@@ -145,5 +151,4 @@ yt_transcribe = gr.Interface(
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with demo:
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gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
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demo.launch()
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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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import tempfile
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import os
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raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
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text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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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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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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ydl_opts = {
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"outtmpl": filename,
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"format": "bestaudio/best",
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"postprocessors": [{
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"key": "FFmpegExtractAudio",
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"preferredcodec": "mp3",
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}]
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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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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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filepath = os.path.join(tmpdirname, "audio.mp3")
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download_yt_audio(yt_url, filepath)
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with open(filepath, "rb") as f:
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inputs = f.read()
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text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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return html_embed_str, text, filepath
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demo = gr.Blocks()
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gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
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gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
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],
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outputs=[
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"html",
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"text",
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gr.Audio(label="Extracted Audio")
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],
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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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with demo:
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gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
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demo.launch()
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