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https://github.com/FunAudioLLM/CosyVoice.git
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update readme
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@@ -26,9 +26,7 @@ For `SenseVoice`, visit [SenseVoice repo](https://github.com/FunAudioLLM/SenseVo
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- [ ] 25hz llama based llm model which supports lora finetune
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- [ ] 25hz llama based llm model which supports lora finetune
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- [ ] Support more instruction mode
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- [ ] Support more instruction mode
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- [ ] Voice conversion
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- [ ] Music generation
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- [ ] Music generation
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- [ ] Training script sample based on Mandarin
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- [ ] CosyVoice-500M trained with more multi-lingual data
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- [ ] CosyVoice-500M trained with more multi-lingual data
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- [ ] More...
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- [ ] More...
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@@ -113,7 +111,7 @@ from cosyvoice.cli.cosyvoice import CosyVoice
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from cosyvoice.utils.file_utils import load_wav
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from cosyvoice.utils.file_utils import load_wav
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import torchaudio
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import torchaudio
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cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-SFT')
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cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-SFT', load_jit=True, load_onnx=False, fp16=True)
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# sft usage
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# sft usage
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print(cosyvoice.list_avaliable_spks())
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print(cosyvoice.list_avaliable_spks())
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# change stream=True for chunk stream inference
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# change stream=True for chunk stream inference
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@@ -74,7 +74,7 @@ def get_args():
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choices=['model_only', 'model+optimizer'],
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choices=['model_only', 'model+optimizer'],
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help='save model/optimizer states')
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help='save model/optimizer states')
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parser.add_argument('--timeout',
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parser.add_argument('--timeout',
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default=30,
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default=60,
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type=int,
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type=int,
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help='timeout (in seconds) of cosyvoice_join.')
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help='timeout (in seconds) of cosyvoice_join.')
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parser = deepspeed.add_config_arguments(parser)
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parser = deepspeed.add_config_arguments(parser)
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@@ -99,7 +99,6 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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--num_workers ${num_workers} \
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--num_workers ${num_workers} \
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--prefetch ${prefetch} \
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--prefetch ${prefetch} \
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--pin_memory \
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--pin_memory \
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--timeout 300 \
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--deepspeed_config ./conf/ds_stage2.json \
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--deepspeed_config ./conf/ds_stage2.json \
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--deepspeed.save_states model+optimizer
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--deepspeed.save_states model+optimizer
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done
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done
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