mrmuminov commited on
Commit
a5bf333
·
1 Parent(s): 42f6a29

Upgrade deps

Browse files
Files changed (3) hide show
  1. README.md +1 -1
  2. app.py +8 -10
  3. requirements.txt +4 -3
README.md CHANGED
@@ -4,7 +4,7 @@ emoji: 🔥
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  colorFrom: pink
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  colorTo: blue
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  sdk: gradio
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- sdk_version: 3.38.0
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  app_file: app.py
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  pinned: false
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  ---
 
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  colorFrom: pink
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  colorTo: blue
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  sdk: gradio
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+ sdk_version: 5.21.0
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  app_file: app.py
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  pinned: false
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  ---
app.py CHANGED
@@ -6,6 +6,7 @@ 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 = "dataprizma/whisper-large-v3-turbo"
@@ -93,11 +94,10 @@ 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=(
@@ -109,11 +109,10 @@ 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.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
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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=(
@@ -125,11 +124,10 @@ file_transcribe = gr.Interface(
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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=(
@@ -141,4 +139,4 @@ 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(enable_queue=True)
 
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  from transformers.pipelines.audio_utils import ffmpeg_read
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  import tempfile
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+ import time
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  import os
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  MODEL_NAME = "dataprizma/whisper-large-v3-turbo"
 
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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"),
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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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  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"),
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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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  yt_transcribe = gr.Interface(
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  fn=yt_transcribe,
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  inputs=[
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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")
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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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  with demo:
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  gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
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+ demo.launch()
requirements.txt CHANGED
@@ -1,3 +1,4 @@
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- git+https://github.com/huggingface/transformers
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- torch
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- yt-dlp
 
 
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+ transformers==4.49.0
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+ torch==2.6.0
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+ yt-dlp==2025.2.19
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+ gradio==5.21.0