mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-05 18:09:24 +08:00
141 lines
4.6 KiB
YAML
141 lines
4.6 KiB
YAML
# set random seed, so that you may reproduce your result.
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__set_seed1: !apply:random.seed [1986]
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__set_seed2: !apply:numpy.random.seed [1986]
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__set_seed3: !apply:torch.manual_seed [1986]
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__set_seed4: !apply:torch.cuda.manual_seed_all [1986]
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# fixed params
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sample_rate: 22050
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text_encoder_input_size: 512
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llm_input_size: 1024
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llm_output_size: 1024
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spk_embed_dim: 192
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# model params
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# for all class/function included in this repo, we use !<name> or !<new> for intialization, so that user may find all corresponding class/function according to one single yaml.
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# for system/third_party class/function, we do not require this.
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hift: !new:cosyvoice.hifigan.generator.HiFTGenerator
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in_channels: 80
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base_channels: 512
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nb_harmonics: 8
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sampling_rate: !ref <sample_rate>
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nsf_alpha: 0.1
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nsf_sigma: 0.003
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nsf_voiced_threshold: 10
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upsample_rates: [8, 8]
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upsample_kernel_sizes: [16, 16]
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istft_params:
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n_fft: 16
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hop_len: 4
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resblock_kernel_sizes: [3, 7, 11]
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resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
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source_resblock_kernel_sizes: [7, 11]
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source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5]]
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lrelu_slope: 0.1
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audio_limit: 0.99
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f0_predictor: !new:cosyvoice.hifigan.f0_predictor.ConvRNNF0Predictor
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num_class: 1
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in_channels: 80
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cond_channels: 512
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mel_spec_transform1: !name:matcha.utils.audio.mel_spectrogram
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n_fft: 1024
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num_mels: 80
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sampling_rate: !ref <sample_rate>
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hop_size: 256
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win_size: 1024
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fmin: 0
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fmax: 8000
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center: False
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hifigan: !new:cosyvoice.hifigan.hifigan.HiFiGan
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generator: !ref <hift>
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discriminator: !new:cosyvoice.hifigan.discriminator.MultipleDiscriminator
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mpd: !new:matcha.hifigan.models.MultiPeriodDiscriminator
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mrd: !new:cosyvoice.hifigan.discriminator.MultiResolutionDiscriminator
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mel_spec_transform: [
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!ref <mel_spec_transform1>
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]
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# processor functions
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parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener
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get_tokenizer: !name:whisper.tokenizer.get_tokenizer # change to !name:cosyvoice.tokenizer.tokenizer.get_tokenizer if you want to train with CosyVoice-300M-25Hz recipe
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multilingual: True
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num_languages: 100
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language: 'en'
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task: 'transcribe'
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tokenize: !name:cosyvoice.dataset.processor.tokenize
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get_tokenizer: !ref <get_tokenizer>
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allowed_special: 'all'
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filter: !name:cosyvoice.dataset.processor.filter
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max_length: 40960
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min_length: 0
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token_max_length: 200
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token_min_length: 1
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resample: !name:cosyvoice.dataset.processor.resample
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resample_rate: !ref <sample_rate>
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truncate: !name:cosyvoice.dataset.processor.truncate
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truncate_length: 24576 # must be a multiplier of hop_size
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feat_extractor: !name:matcha.utils.audio.mel_spectrogram
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n_fft: 1024
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num_mels: 80
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sampling_rate: !ref <sample_rate>
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hop_size: 256
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win_size: 1024
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fmin: 0
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fmax: 8000
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center: False
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compute_fbank: !name:cosyvoice.dataset.processor.compute_fbank
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feat_extractor: !ref <feat_extractor>
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pitch_extractor: !name:torchaudio.functional.compute_kaldi_pitch
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sample_rate: !ref <sample_rate>
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frame_length: 46.4 # match feat_extractor win_size/sampling_rate
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frame_shift: 11.6 # match feat_extractor hop_size/sampling_rate
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compute_f0: !name:cosyvoice.dataset.processor.compute_f0
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pitch_extractor: !ref <pitch_extractor>
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parse_embedding: !name:cosyvoice.dataset.processor.parse_embedding
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normalize: True
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shuffle: !name:cosyvoice.dataset.processor.shuffle
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shuffle_size: 1000
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sort: !name:cosyvoice.dataset.processor.sort
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sort_size: 500 # sort_size should be less than shuffle_size
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batch: !name:cosyvoice.dataset.processor.batch
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batch_type: 'dynamic'
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max_frames_in_batch: 1200
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padding: !name:cosyvoice.dataset.processor.padding
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use_spk_embedding: False # change to True during sft
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# dataset processor pipeline
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data_pipeline: [
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!ref <parquet_opener>,
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!ref <tokenize>,
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!ref <filter>,
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!ref <resample>,
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!ref <truncate>,
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!ref <compute_fbank>,
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!ref <compute_f0>,
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!ref <parse_embedding>,
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!ref <shuffle>,
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!ref <sort>,
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!ref <batch>,
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!ref <padding>,
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]
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# train conf
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train_conf:
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optim: adam
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optim_conf:
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lr: 0.002 # change to 0.001 if you want to train flow from scratch
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 25000
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optim_d: adam
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optim_conf_d:
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lr: 0.002 # change to 0.001 if you want to train flow from scratch
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scheduler_d: warmuplr
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scheduler_conf_d:
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warmup_steps: 25000
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max_epoch: 200
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grad_clip: 5
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accum_grad: 2
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log_interval: 100
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save_per_step: -1 |