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Upload 2 files
Browse files- app.py +11 -19
- requirements.txt +2 -2
app.py
CHANGED
@@ -3,7 +3,6 @@ import streamlit as st
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import time
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import os
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os.environ['COQUI_TOS_AGREED'] = '1'
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os.environ['BARK_TOS_AGREED'] = '1'
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@@ -19,11 +18,9 @@ def model_coqui():
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# @st.cache_resource()
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def model_bark():
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return processor, model
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col1, col2 = st.columns(2)
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@@ -59,19 +56,14 @@ if speaker:
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st.audio(output_path_coqui, format="audio/mp3")
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with col22:
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start_time_bark = time.time() # Start the timer
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audio_array = model.generate(**inputs)
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end_time = time.time() # End the timer
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audio_array = audio_array.cpu().numpy().squeeze()
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sample_rate = model.generation_config.sample_rate
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execution_time = round(end_time - start_time_bark,2)
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st.write(f"Bark
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st.audio(
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import time
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import os
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os.environ['COQUI_TOS_AGREED'] = '1'
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os.environ['BARK_TOS_AGREED'] = '1'
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# @st.cache_resource()
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def model_bark():
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device = "cpu"
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tts = TTS("tts_models/multilingual/multi-dataset/bark").to(device)
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return tts
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col1, col2 = st.columns(2)
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st.audio(output_path_coqui, format="audio/mp3")
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with col22:
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tts_bark = model_bark()
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speaker_wav_2 = "speaker_embeddings/tr_speaker_0"
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start_time_bark = time.time()
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output_path_bark = tts_bark.tts_to_file(text=sentence, speaker_wav=speaker_wav_2)
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end_time = time.time()
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execution_time = round(end_time - start_time_bark,2)
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st.write(f"Bark içi tamamlanma süresi: {execution_time} saniye")
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st.audio(output_path_bark, format="audio/mp3")
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requirements.txt
CHANGED
@@ -1,3 +1,3 @@
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streamlit
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TTS
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torch
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streamlit
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git+https://github.com/coqui-ai/TTS
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torch
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