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Uploading model.

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+ # 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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+
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+ # fixed params
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+ sample_rate: 24000
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+ llm_input_size: 896
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+ llm_output_size: 896
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+ spk_embed_dim: 192
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+ qwen_pretrain_path: ''
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+ token_frame_rate: 25
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+ token_mel_ratio: 2
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+
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+ # stream related params
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+ chunk_size: 25 # streaming inference chunk size, in token
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+ num_decoding_left_chunks: -1 # streaming inference flow decoder left chunk size, <0 means use all left chunks
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+
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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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+ llm: !new:cosyvoice.llm.llm.CosyVoice3LM
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+ llm_input_size: !ref <llm_input_size>
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+ llm_output_size: !ref <llm_output_size>
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+ speech_token_size: 6561
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+ length_normalized_loss: True
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+ lsm_weight: 0
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+ mix_ratio: [5, 15]
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+ llm: !new:cosyvoice.llm.llm.Qwen2Encoder
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+ pretrain_path: !ref <qwen_pretrain_path>
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+ sampling: !name:cosyvoice.utils.common.ras_sampling
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+ top_p: 0.8
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+ top_k: 25
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+ win_size: 10
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+ tau_r: 0.1
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+
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+ flow: !new:cosyvoice.flow.flow.CausalMaskedDiffWithDiT
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+ input_size: 80
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+ output_size: 80
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+ spk_embed_dim: !ref <spk_embed_dim>
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+ output_type: 'mel'
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+ vocab_size: 6561
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+ input_frame_rate: !ref <token_frame_rate>
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+ only_mask_loss: True
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+ token_mel_ratio: !ref <token_mel_ratio>
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+ pre_lookahead_len: 3
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+ pre_lookahead_layer: !new:cosyvoice.transformer.upsample_encoder.PreLookaheadLayer
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+ in_channels: 80
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+ channels: 1024
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+ pre_lookahead_len: 3
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+ decoder: !new:cosyvoice.flow.flow_matching.CausalConditionalCFM
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+ in_channels: 240
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+ n_spks: 1
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+ spk_emb_dim: 80
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+ cfm_params: !new:omegaconf.DictConfig
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+ content:
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+ sigma_min: 1e-06
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+ solver: 'euler'
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+ t_scheduler: 'cosine'
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+ training_cfg_rate: 0.2
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+ inference_cfg_rate: 0.7
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+ reg_loss_type: 'l1'
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+ estimator: !new:cosyvoice.flow.DiT.dit.DiT
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+ dim: 1024
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+ depth: 22
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+ heads: 16
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+ dim_head: 64
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+ ff_mult: 2
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+ mel_dim: 80
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+ mu_dim: 80
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+ spk_dim: 80
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+ out_channels: 80
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+ static_chunk_size: !ref <chunk_size> * <token_mel_ratio>
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+ num_decoding_left_chunks: !ref <num_decoding_left_chunks>
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+
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+ hift: !new:cosyvoice.hifigan.generator.CausalHiFTGenerator
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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, 5, 3]
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+ upsample_kernel_sizes: [16, 11, 7]
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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, 7, 11]
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+ source_resblock_dilation_sizes: [[1, 3, 5], [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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+ conv_pre_look_right: 4
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+ f0_predictor: !new:cosyvoice.hifigan.f0_predictor.CausalConvRNNF0Predictor
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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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+
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+ # gan related module
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+ mel_spec_transform1: !name:matcha.utils.audio.mel_spectrogram
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+ n_fft: 1920
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+ num_mels: 80
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+ sampling_rate: !ref <sample_rate>
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+ hop_size: 480
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+ win_size: 1920
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+ fmin: 0
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+ fmax: null
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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.MultiResSpecDiscriminator
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+ mel_spec_transform: [
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+ !ref <mel_spec_transform1>
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+ ]
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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:cosyvoice.tokenizer.tokenizer.get_qwen_tokenizer
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+ token_path: !ref <qwen_pretrain_path>
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+ skip_special_tokens: True
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+ version: cosyvoice3
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+ allowed_special: 'all'
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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: !ref <allowed_special>
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+ filter: !name:cosyvoice.dataset.processor.filter
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+ max_length: 40960
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+ min_length: 100
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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: 24480 # 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: 1920
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+ num_mels: 80
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+ sampling_rate: !ref <sample_rate>
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+ hop_size: 480
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+ win_size: 1920
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+ fmin: 0
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+ fmax: null
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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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+ compute_f0: !name:cosyvoice.dataset.processor.compute_f0
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+ sample_rate: !ref <sample_rate>
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+ hop_size: 480
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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: 2000
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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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+
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+
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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 <compute_fbank>,
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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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+ data_pipeline_gan: [
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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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+
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+ # llm flow 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: 1e-5 # change to 1e-5 during sft
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+ scheduler: constantlr # change to constantlr during sft
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+ scheduler_conf:
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+ warmup_steps: 2500
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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
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+
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+ # gan train conf
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+ train_conf_gan:
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+ optim: adam
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+ optim_conf:
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+ lr: 0.0002 # use small lr for gan training
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+ scheduler: constantlr
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+ optim_d: adam
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+ optim_conf_d:
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+ lr: 0.0002 # use small lr for gan training
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+ scheduler_d: constantlr
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+ max_epoch: 200
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+ grad_clip: 5
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+ accum_grad: 1 # in gan training, accum_grad must be 1
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+ log_interval: 100
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+ save_per_step: -1
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