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31 lines
1.6 KiB
31 lines
1.6 KiB
from TTS.api import TTS |
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import torch |
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from datetime import datetime |
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tts = TTS("tts_models/en/multi-dataset/tortoise-v2") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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tts.to(device) |
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text="There is, therefore, an increasing need to understand BEVs from a systems perspective. This involves an in-depth consideration of the environmental impact of the product using life cycle assessment (LCA) as well as taking a broader 'circular economy' approach. On the one hand, LCA is a means of assessing the environmental impact associated with all stages of a product's life from cradle to grave: from raw material extraction and processing to the product's manufacture to its use in everyday life and finally to its end of life." |
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# cloning `lj` voice from `TTS/tts/utils/assets/tortoise/voices/lj` |
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# with custom inference settings overriding defaults. |
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time_now = datetime.now().strftime("%Y%m%d%H%M%S") |
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output_path = f"output/tortoise_{time_now}.wav" |
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tts.tts_to_file(text, |
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file_path=output_path, |
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voice_dir="voices", |
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speaker="test", |
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split_sentences=False, # Change to True if context is not enough |
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num_autoregressive_samples=20, |
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diffusion_iterations=50) |
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# # Using presets with the same voice |
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# tts.tts_to_file(text, |
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# file_path="output.wav", |
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# voice_dir="path/to/tortoise/voices/dir/", |
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# speaker="lj", |
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# preset="ultra_fast") |
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# # Random voice generation |
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# tts.tts_to_file(text, |
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# file_path="output.wav") |