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Create app.py
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app.py
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import gradio as gr
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import torchaudio
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import torch
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import os
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import time
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import soundfile as sf
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welcome_message = """
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# Welcome to Tonic's Unity On Device!
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Tonic's Unity On Device uses [facebook/seamless-m4t-unity-small](https://huggingface.co/facebook/seamless-m4t-unity-small) for audio translation & accessibility.
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Tonic's Unity On Device!🚀 on your own data & in your own way by cloning this space. Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/TeamTonic/SeamlessOnDevice?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3>
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### Join us :
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TeamTonic is always making cool demos! Join our active builder's community on Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On ðŸŒGithub: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
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"""
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# Define the list of target languages
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languages = {
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"English": "eng",
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"Hindi": "hin",
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"Portuguese": "por",
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"Russian": "rus",
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"Spanish": "spa"
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}
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def save_audio(audio_input, output_dir="saved_audio"):
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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# Extract sample rate and audio data
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sample_rate, audio_data = audio_input
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# Generate a unique file name
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file_name = f"audio_{int(time.time())}.wav"
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file_path = os.path.join(output_dir, file_name)
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# Save the audio file
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sf.write(file_path, audio_data, sample_rate)
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return file_path
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def speech_to_text(audio_data, tgt_lang):
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file_path = save_audio(audio_data)
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audio_input, _ = torchaudio.load(file_path)
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s2t_model = torch.jit.load("unity_on_device_s2t.ptl")
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with torch.no_grad():
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text = s2t_model(audio_input, tgt_lang=languages[tgt_lang])
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return text
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def speech_to_speech_translation(audio_data, tgt_lang):
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file_path = save_audio(audio_data)
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audio_input, _ = torchaudio.load(file_path)
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s2st_model = torch.jit.load("unity_on_device.ptl")
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with torch.no_grad():
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text, units, waveform = s2st_model(audio_input, tgt_lang=languages[tgt_lang])
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output_file = "/tmp/result.wav"
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torchaudio.save(output_file, waveform.unsqueeze(0), sample_rate=16000)
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return text, output_file
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def create_interface():
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with gr.Markdown(welcome_message)
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with gr.Blocks(theme='ParityError/Anime') as interface:
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# Dropdown for language selection
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input_language = gr.Dropdown(list(languages.keys()), label="Select Target Language", value="English")
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with gr.Accordion("Speech to Text", open=False) as stt_accordion:
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audio_input_stt = gr.Audio(label="Upload or Record Audio")
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text_output_stt = gr.Text(label="Transcribed Text")
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stt_button = gr.Button("Transcribe")
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stt_button.click(speech_to_text, inputs=[audio_input_stt, input_language], outputs=text_output_stt)
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with gr.Accordion("Speech to Speech Translation", open=False) as s2st_accordion:
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audio_input_s2st = gr.Audio(label="Upload or Record Audio")
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text_output_s2st = gr.Text(label="Translated Text")
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audio_output_s2st = gr.Audio(label="Translated Audio", type="filepath")
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s2st_button = gr.Button("Translate")
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s2st_button.click(speech_to_speech_translation, inputs=[audio_input_s2st, input_language], outputs=[text_output_s2st, audio_output_s2st])
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return interface
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app = create_interface()
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app.launch(show_error=True, debug=True)
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