Update app.py
Browse files
app.py
CHANGED
@@ -8,11 +8,17 @@ from huggingface_hub import snapshot_download, login
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import logging
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import os
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import spaces
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# Set up logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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def get_device():
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if torch.cuda.is_available():
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return torch.device("cuda")
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@@ -119,6 +125,8 @@ def text_to_speech(text, voice):
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raise
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def mel_to_audio(mel):
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return np.zeros(24000, dtype=np.float32) # Placeholder: 1 second of silence
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@spaces.GPU()
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@@ -140,6 +148,11 @@ def render_podcast(api_key, script, voice1, voice2, num_hosts):
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return (24000, np.zeros(24000, dtype=np.float32))
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podcast_audio = np.concatenate(audio_segments)
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return (24000, podcast_audio)
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except Exception as e:
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logger.error(f"Error rendering podcast: {str(e)}")
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import logging
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import os
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import spaces
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import warnings
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# Set up logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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# Suppress specific warnings
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warnings.filterwarnings("ignore", category=UserWarning, message="Trying to convert audio automatically from float32 to 16-bit int format.")
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warnings.filterwarnings("ignore", category=RuntimeWarning, message="invalid value encountered in divide")
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warnings.filterwarnings("ignore", category=RuntimeWarning, message="invalid value encountered in cast")
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def get_device():
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if torch.cuda.is_available():
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return torch.device("cuda")
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raise
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def mel_to_audio(mel):
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# Implement proper mel to audio conversion here
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# For now, we'll return a placeholder audio
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return np.zeros(24000, dtype=np.float32) # Placeholder: 1 second of silence
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@spaces.GPU()
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return (24000, np.zeros(24000, dtype=np.float32))
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podcast_audio = np.concatenate(audio_segments)
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# Ensure the audio is in the correct format for Gradio
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podcast_audio = np.clip(podcast_audio, -1, 1)
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podcast_audio = (podcast_audio * 32767).astype(np.int16)
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return (24000, podcast_audio)
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except Exception as e:
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logger.error(f"Error rendering podcast: {str(e)}")
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