Update app.py
Browse files
app.py
CHANGED
@@ -117,11 +117,12 @@ def speech_to_text(video_file_path, selected_source_lang, whisper_model):
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This space allows you to:
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1. Download youtube video with a given url
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2. Watch it in the first video component
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3. Run automatic speech recognition on the video using Whisper
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4. Translate the recognized transcriptions to
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5. Burn the translations to the original video and watch the video in the 2nd video component
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Speech Recognition is based on OpenAI Whisper https://github.com/openai/whisper
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"""
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if(video_file_path == None):
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@@ -308,14 +309,14 @@ with demo:
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### This space allows you to:
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##### 1. Download youtube video with a given URL
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##### 2. Watch it in the first video component
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##### 3. Run automatic speech recognition on the video using Whisper
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##### 4. Translate the recognized transcriptions to
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##### 5. Burn the translations to the original video and watch the video in the 2nd video component
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''')
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with gr.Column():
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gr.Markdown('''
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### 1. Insert Youtube URL below
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##### 1. https://www.youtube.com/watch?v=nlMuHtV82q8&ab_channel=NothingforSale24
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##### 2. https://www.youtube.com/watch?v=JzPfMbG1vrE&ab_channel=ExplainerVideosByLauren
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##### 3. https://www.youtube.com/watch?v=S68vvV0kod8&ab_channel=Pearl-CohnTelevision
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@@ -336,7 +337,7 @@ with demo:
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with gr.Column():
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gr.Markdown('''
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##### Here you can start the transcription and translation process.
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##### Be aware that processing will last
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''')
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selected_source_lang.render()
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selected_whisper_model.render()
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@@ -357,7 +358,7 @@ with demo:
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with gr.Column():
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gr.Markdown('''
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##### Here you will get translated transcriptions.
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##### Please remember to select
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##### ''')
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selected_translation_lang_2.render()
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translate_transcriptions_button = gr.Button("Step 3. Translate transcription")
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This space allows you to:
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1. Download youtube video with a given url
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2. Watch it in the first video component
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3. Run automatic speech recognition on the video using fast Whisper models
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4. Translate the recognized transcriptions to 26 languages supported by deepL
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5. Burn the translations to the original video and watch the video in the 2nd video component
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Speech Recognition is based on models from OpenAI Whisper https://github.com/openai/whisper
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This space is using c++ implementation by https://github.com/ggerganov/whisper.cpp
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"""
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if(video_file_path == None):
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### This space allows you to:
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##### 1. Download youtube video with a given URL
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##### 2. Watch it in the first video component
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+
##### 3. Run automatic speech recognition on the video using Whisper
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##### 4. Translate the recognized transcriptions to 26 languages supported by deepL
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##### 5. Burn the translations to the original video and watch the video in the 2nd video component
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''')
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with gr.Column():
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gr.Markdown('''
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### 1. Insert Youtube URL below. Some test videos below:
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##### 1. https://www.youtube.com/watch?v=nlMuHtV82q8&ab_channel=NothingforSale24
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##### 2. https://www.youtube.com/watch?v=JzPfMbG1vrE&ab_channel=ExplainerVideosByLauren
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##### 3. https://www.youtube.com/watch?v=S68vvV0kod8&ab_channel=Pearl-CohnTelevision
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with gr.Column():
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gr.Markdown('''
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##### Here you can start the transcription and translation process.
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##### Be aware that processing will last some time. With base model it is around 3x speed
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''')
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selected_source_lang.render()
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selected_whisper_model.render()
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with gr.Column():
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gr.Markdown('''
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##### Here you will get translated transcriptions.
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##### Please remember to select target language
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##### ''')
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selected_translation_lang_2.render()
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translate_transcriptions_button = gr.Button("Step 3. Translate transcription")
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