CosyVoice/examples/libritts/cosyvoice3/conf/cosyvoice3.yaml

# set random seed, so that you may reproduce your result.
__set_seed1: !apply:random.seed [1986]
__set_seed2: !apply:numpy.random.seed [1986]
__set_seed3: !apply:torch.manual_seed [1986]
__set_seed4: !apply:torch.cuda.manual_seed_all [1986]

# fixed params
sample_rate: 24000
llm_input_size: 896
llm_output_size: 896
spk_embed_dim: 192
qwen_pretrain_path: ''
token_frame_rate: 25
token_mel_ratio: 2

# stream related params
chunk_size: 25 # streaming inference chunk size, in token
num_decoding_left_chunks: -1 # streaming inference flow decoder left chunk size, <0 means use all left chunks

# model params
# 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.
# for system/third_party class/function, we do not require this.
llm: !new:cosyvoice.llm.llm.CosyVoice3LM
    llm_input_size: !ref <llm_input_size>
    llm_output_size: !ref <llm_output_size>
    speech_token_size: 6561
    length_normalized_loss: True
    lsm_weight: 0
    mix_ratio: [5, 15]
    llm: !new:cosyvoice.llm.llm.Qwen2Encoder
        pretrain_path: !ref <qwen_pretrain_path>
    sampling: !name:cosyvoice.utils.common.ras_sampling
        top_p: 0.8
        top_k: 25
        win_size: 10
        tau_r: 0.1

flow: !new:cosyvoice.flow.flow.CausalMaskedDiffWithDiT
    input_size: 80
    output_size: 80
    spk_embed_dim: !ref <spk_embed_dim>
    output_type: 'mel'
    vocab_size: 6561
    input_frame_rate: !ref <token_frame_rate>
    only_mask_loss: True
    token_mel_ratio: !ref <token_mel_ratio>
    pre_lookahead_len: 3
    pre_lookahead_layer: !new:cosyvoice.transformer.upsample_encoder.PreLookaheadLayer
        in_channels: 80
        channels: 1024
        pre_lookahead_len: 3
    decoder: !new:cosyvoice.flow.flow_matching.CausalConditionalCFM
        in_channels: 240
        n_spks: 1
        spk_emb_dim: 80
        cfm_params: !new:omegaconf.DictConfig
            content:
                sigma_min: 1e-06
                solver: 'euler'
                t_scheduler: 'cosine'
                training_cfg_rate: 0.2
                inference_cfg_rate: 0.7
                reg_loss_type: 'l1'
        estimator: !new:cosyvoice.flow.DiT.dit.DiT
            dim: 1024
            depth: 22
            heads: 16
            dim_head: 64
            ff_mult: 2
            mel_dim: 80
            mu_dim: 80
            spk_dim: 80
            out_channels: 80
            static_chunk_size: !ref <chunk_size> * <token_mel_ratio>
            num_decoding_left_chunks: !ref <num_decoding_left_chunks>

hift: !new:cosyvoice.hifigan.generator.CausalHiFTGenerator
    in_channels: 80
    base_channels: 512
    nb_harmonics: 8
    sampling_rate: !ref <sample_rate>
    nsf_alpha: 0.1
    nsf_sigma: 0.003
    nsf_voiced_threshold: 10
    upsample_rates: [8, 5, 3]
    upsample_kernel_sizes: [16, 11, 7]
    istft_params:
        n_fft: 16
        hop_len: 4
    resblock_kernel_sizes: [3, 7, 11]
    resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
    source_resblock_kernel_sizes: [7, 7, 11]
    source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
    lrelu_slope: 0.1
    audio_limit: 0.99
    conv_pre_look_right: 4
    f0_predictor: !new:cosyvoice.hifigan.f0_predictor.CausalConvRNNF0Predictor
        num_class: 1
        in_channels: 80
        cond_channels: 512

# gan related module
mel_spec_transform1: !name:matcha.utils.audio.mel_spectrogram
    n_fft: 1920
    num_mels: 80
    sampling_rate: !ref <sample_rate>
    hop_size: 480
    win_size: 1920
    fmin: 0
    fmax: null
    center: False
hifigan: !new:cosyvoice.hifigan.hifigan.HiFiGan
    generator: !ref <hift>
    discriminator: !new:cosyvoice.hifigan.discriminator.MultipleDiscriminator
        mpd: !new:matcha.hifigan.models.MultiPeriodDiscriminator
        mrd: !new:cosyvoice.hifigan.discriminator.MultiResSpecDiscriminator
    mel_spec_transform: [
        !ref <mel_spec_transform1>
    ]

# processor functions
parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener
get_tokenizer: !name:cosyvoice.tokenizer.tokenizer.get_qwen_tokenizer
    token_path: !ref <qwen_pretrain_path>
    skip_special_tokens: True
    version: cosyvoice3
allowed_special: 'all'
tokenize: !name:cosyvoice.dataset.processor.tokenize
    get_tokenizer: !ref <get_tokenizer>
    allowed_special: !ref <allowed_special>
filter: !name:cosyvoice.dataset.processor.filter
    max_length: 6000
    min_length: 100
    token_max_length: 200
    token_min_length: 1
resample: !name:cosyvoice.dataset.processor.resample
    resample_rate: !ref <sample_rate>
truncate: !name:cosyvoice.dataset.processor.truncate
    truncate_length: 24960 # must be a multiplier of hop_size and token_mel_ratio
feat_extractor: !name:matcha.utils.audio.mel_spectrogram
    n_fft: 1920
    num_mels: 80
    sampling_rate: !ref <sample_rate>
    hop_size: 480
    win_size: 1920
    fmin: 0
    fmax: null
    center: False
compute_fbank: !name:cosyvoice.dataset.processor.compute_fbank
    feat_extractor: !ref <feat_extractor>
    num_frames: 960
compute_whisper_fbank: !name:cosyvoice.dataset.processor.compute_whisper_fbank
    num_frames: 960
compute_f0: !name:cosyvoice.dataset.processor.compute_f0
    sample_rate: !ref <sample_rate>
    hop_size: 480
parse_embedding: !name:cosyvoice.dataset.processor.parse_embedding
    normalize: True
shuffle: !name:cosyvoice.dataset.processor.shuffle
    shuffle_size: 1000
sort: !name:cosyvoice.dataset.processor.sort
    sort_size: 500  # sort_size should be less than shuffle_size
batch: !name:cosyvoice.dataset.processor.batch
    batch_type: 'dynamic'
    max_frames_in_batch: 2000
padding: !name:cosyvoice.dataset.processor.padding
    use_spk_embedding: False # change to True during sft


# dataset processor pipeline
data_pipeline: [
    !ref <parquet_opener>,
    !ref <tokenize>,
    !ref <filter>,
    !ref <resample>,
    !ref <compute_fbank>,
    !ref <parse_embedding>,
    !ref <compute_whisper_fbank>,
    !ref <shuffle>,
    !ref <sort>,
    !ref <batch>,
    !ref <padding>,
]
data_pipeline_gan: [
    !ref <parquet_opener>,
    !ref <tokenize>,
    !ref <filter>,
    !ref <resample>,
    !ref <truncate>,
    !ref <compute_fbank>,
    !ref <compute_f0>,
    !ref <parse_embedding>,
    !ref <shuffle>,
    !ref <sort>,
    !ref <batch>,
    !ref <padding>,
]

# llm flow train conf
train_conf:
    optim: adam
    optim_conf:
        lr: 1e-5 # change to 1e-5 during sft
    scheduler: constantlr # change to constantlr during sft
    scheduler_conf:
        warmup_steps: 2500
    max_epoch: 200
    grad_clip: 5
    accum_grad: 2
    log_interval: 100
    save_per_step: -1

# gan train conf
train_conf_gan:
    optim: adam
    optim_conf:
        lr: 0.0002 # use small lr for gan training
    scheduler: constantlr
    optim_d: adam
    optim_conf_d:
        lr: 0.0002 # use small lr for gan training
    scheduler_d: constantlr
    max_epoch: 200
    grad_clip: 5
    accum_grad: 1 # in gan training, accum_grad must be 1
    log_interval: 100
    save_per_step: -1