TianYuan
3 years ago
parent
b9dc017011
commit
8d025451de
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###########################################################
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# FEATURE EXTRACTION SETTING #
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###########################################################
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fs: 24000 # sr
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n_fft: 2048 # FFT size.
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n_shift: 300 # Hop size.
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win_length: 1200 # Window length.
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# If set to null, it will be the same as fft_size.
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window: "hann" # Window function.
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# Only used for feats_type != raw
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fmin: 80 # Minimum frequency of Mel basis.
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fmax: 7600 # Maximum frequency of Mel basis.
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n_mels: 80 # The number of mel basis.
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# Only used for the model using pitch features (e.g. FastSpeech2)
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f0min: 80 # Maximum f0 for pitch extraction.
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f0max: 400 # Minimum f0 for pitch extraction.
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###########################################################
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# DATA SETTING #
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###########################################################
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batch_size: 64
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num_workers: 2
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###########################################################
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# MODEL SETTING #
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###########################################################
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model:
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adim: 384 # attention dimension
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aheads: 2 # number of attention heads
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elayers: 4 # number of encoder layers
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eunits: 1536 # number of encoder ff units
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dlayers: 4 # number of decoder layers
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dunits: 1536 # number of decoder ff units
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positionwise_layer_type: conv1d # type of position-wise layer
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positionwise_conv_kernel_size: 3 # kernel size of position wise conv layer
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duration_predictor_layers: 2 # number of layers of duration predictor
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duration_predictor_chans: 256 # number of channels of duration predictor
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duration_predictor_kernel_size: 3 # filter size of duration predictor
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postnet_layers: 5 # number of layers of postnset
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postnet_filts: 5 # filter size of conv layers in postnet
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postnet_chans: 256 # number of channels of conv layers in postnet
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use_masking: True # whether to apply masking for padded part in loss calculation
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use_scaled_pos_enc: True # whether to use scaled positional encoding
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encoder_normalize_before: True # whether to perform layer normalization before the input
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decoder_normalize_before: True # whether to perform layer normalization before the input
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reduction_factor: 1 # reduction factor
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init_type: xavier_uniform # initialization type
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init_enc_alpha: 1.0 # initial value of alpha of encoder scaled position encoding
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init_dec_alpha: 1.0 # initial value of alpha of decoder scaled position encoding
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transformer_enc_dropout_rate: 0.2 # dropout rate for transformer encoder layer
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transformer_enc_positional_dropout_rate: 0.2 # dropout rate for transformer encoder positional encoding
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transformer_enc_attn_dropout_rate: 0.2 # dropout rate for transformer encoder attention layer
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transformer_dec_dropout_rate: 0.2 # dropout rate for transformer decoder layer
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transformer_dec_positional_dropout_rate: 0.2 # dropout rate for transformer decoder positional encoding
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transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
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pitch_predictor_layers: 5 # number of conv layers in pitch predictor
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pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
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pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
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pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
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pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
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pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
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stop_gradient_from_pitch_predictor: true # whether to stop the gradient from pitch predictor to encoder
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energy_predictor_layers: 2 # number of conv layers in energy predictor
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energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
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energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
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energy_predictor_dropout: 0.5 # dropout rate in energy predictor
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energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
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energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy
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stop_gradient_from_energy_predictor: false # whether to stop the gradient from energy predictor to encoder
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spk_embed_dim: 256 # speaker embedding dimension
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spk_embed_integration_type: concat # speaker embedding integration type
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###########################################################
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# UPDATER SETTING #
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###########################################################
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updater:
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use_masking: True # whether to apply masking for padded part in loss calculation
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###########################################################
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# OPTIMIZER SETTING #
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###########################################################
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optimizer:
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optim: adam # optimizer type
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learning_rate: 0.001 # learning rate
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###########################################################
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# TRAINING SETTING #
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###########################################################
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max_epoch: 200
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num_snapshots: 5
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###########################################################
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# OTHER SETTING #
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###########################################################
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seed: 10086
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#!/bin/bash
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stage=0
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stop_stage=100
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config_path=$1
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ge2e_ckpt_path=$2
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# gen speaker embedding
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${MAIN_ROOT}/paddlespeech/vector/exps/ge2e/inference.py \
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--input=~/datasets/data_aishell3/train/wav/ \
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--output=dump/embed \
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--checkpoint_path=${ge2e_ckpt_path}
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fi
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# copy from tts3/preprocess
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# get durations from MFA's result
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echo "Generate durations.txt from MFA results ..."
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python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
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--inputdir=./aishell3_alignment_tone \
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--output durations.txt \
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--config=${config_path}
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# extract features
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echo "Extract features ..."
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python3 ${BIN_DIR}/preprocess.py \
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--dataset=aishell3 \
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--rootdir=~/datasets/data_aishell3/ \
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--dumpdir=dump \
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--dur-file=durations.txt \
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--config=${config_path} \
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--num-cpu=20 \
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--cut-sil=True \
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--embed-dir=dump/embed
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# get features' stats(mean and std)
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echo "Get features' stats ..."
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python3 ${MAIN_ROOT}/utils/compute_statistics.py \
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--metadata=dump/train/raw/metadata.jsonl \
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--field-name="speech"
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python3 ${MAIN_ROOT}/utils/compute_statistics.py \
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--metadata=dump/train/raw/metadata.jsonl \
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--field-name="pitch"
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python3 ${MAIN_ROOT}/utils/compute_statistics.py \
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--metadata=dump/train/raw/metadata.jsonl \
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--field-name="energy"
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fi
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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# normalize and covert phone/speaker to id, dev and test should use train's stats
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echo "Normalize ..."
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python3 ${BIN_DIR}/normalize.py \
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--metadata=dump/train/raw/metadata.jsonl \
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--dumpdir=dump/train/norm \
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--speech-stats=dump/train/speech_stats.npy \
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--pitch-stats=dump/train/pitch_stats.npy \
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--energy-stats=dump/train/energy_stats.npy \
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--phones-dict=dump/phone_id_map.txt \
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--speaker-dict=dump/speaker_id_map.txt
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python3 ${BIN_DIR}/normalize.py \
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--metadata=dump/dev/raw/metadata.jsonl \
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--dumpdir=dump/dev/norm \
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--speech-stats=dump/train/speech_stats.npy \
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--pitch-stats=dump/train/pitch_stats.npy \
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--energy-stats=dump/train/energy_stats.npy \
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--phones-dict=dump/phone_id_map.txt \
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--speaker-dict=dump/speaker_id_map.txt
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python3 ${BIN_DIR}/normalize.py \
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--metadata=dump/test/raw/metadata.jsonl \
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--dumpdir=dump/test/norm \
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--speech-stats=dump/train/speech_stats.npy \
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--pitch-stats=dump/train/pitch_stats.npy \
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--energy-stats=dump/train/energy_stats.npy \
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--phones-dict=dump/phone_id_map.txt \
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--speaker-dict=dump/speaker_id_map.txt
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fi
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#!/bin/bash
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config_path=$1
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train_output_path=$2
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ckpt_name=$3
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/synthesize.py \
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--fastspeech2-config=${config_path} \
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--fastspeech2-checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
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--fastspeech2-stat=dump/train/speech_stats.npy \
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--pwg-config=pwg_aishell3_ckpt_0.5/default.yaml \
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--pwg-checkpoint=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
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--pwg-stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
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--test-metadata=dump/test/norm/metadata.jsonl \
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--output-dir=${train_output_path}/test \
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--phones-dict=dump/phone_id_map.txt \
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--voice-cloning=True
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#!/bin/bash
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config_path=$1
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train_output_path=$2
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python3 ${BIN_DIR}/train.py \
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--train-metadata=dump/train/norm/metadata.jsonl \
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--dev-metadata=dump/dev/norm/metadata.jsonl \
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--config=${config_path} \
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--output-dir=${train_output_path} \
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--ngpu=2 \
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--phones-dict=dump/phone_id_map.txt \
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--voice-cloning=True
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#!/bin/bash
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config_path=$1
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train_output_path=$2
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ckpt_name=$3
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ge2e_params_path=$4
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ref_audio_dir=$5
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/voice_cloning.py \
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--fastspeech2-config=${config_path} \
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--fastspeech2-checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
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--fastspeech2-stat=dump/train/speech_stats.npy \
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--pwg-config=pwg_aishell3_ckpt_0.5/default.yaml \
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--pwg-checkpoint=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
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--pwg-stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
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--ge2e_params_path=${ge2e_params_path} \
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--text="凯莫瑞安联合体的经济崩溃迫在眉睫。" \
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--input-dir=${ref_audio_dir} \
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--output-dir=${train_output_path}/vc_syn \
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--phones-dict=dump/phone_id_map.txt
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#!/bin/bash
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export MAIN_ROOT=`realpath ${PWD}/../../../`
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export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
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export LC_ALL=C
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export PYTHONDONTWRITEBYTECODE=1
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# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
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export PYTHONIOENCODING=UTF-8
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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=fastspeech2
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}
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#!/bin/bash
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set -e
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source path.sh
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gpus=0,1
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stage=0
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stop_stage=100
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conf_path=conf/default.yaml
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train_output_path=exp/default
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ckpt_name=snapshot_iter_482.pdz
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ref_audio_dir=ref_audio
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# not include ".pdparams" here
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ge2e_ckpt_path=./ge2e_ckpt_0.3/step-3000000
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# include ".pdparams" here
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ge2e_params_path=${ge2e_ckpt_path}.pdparams
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# with the following command, you can choice the stage range you want to run
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# such as `./run.sh --stage 0 --stop-stage 0`
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# this can not be mixed use with `$1`, `$2` ...
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source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# prepare data
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CUDA_VISIBLE_DEVICES=${gpus} ./local/preprocess.sh ${conf_path} ${ge2e_ckpt_path} || exit -1
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# train model, all `ckpt` under `train_output_path/checkpoints/` dir
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# synthesize, vocoder is pwgan
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# synthesize, vocoder is pwgan
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CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} ${ge2e_params_path} ${ref_audio_dir} || exit -1
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fi
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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from pathlib import Path
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import numpy as np
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import paddle
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import soundfile as sf
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import yaml
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from yacs.config import CfgNode
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from paddlespeech.t2s.frontend.zh_frontend import Frontend
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2Inference
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from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
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from paddlespeech.t2s.models.parallel_wavegan import PWGInference
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from paddlespeech.t2s.modules.normalizer import ZScore
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from paddlespeech.vector.exps.ge2e.audio_processor import SpeakerVerificationPreprocessor
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from paddlespeech.vector.models.lstm_speaker_encoder import LSTMSpeakerEncoder
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def voice_cloning(args, fastspeech2_config, pwg_config):
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# speaker encoder
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p = SpeakerVerificationPreprocessor(
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sampling_rate=16000,
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audio_norm_target_dBFS=-30,
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vad_window_length=30,
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vad_moving_average_width=8,
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vad_max_silence_length=6,
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mel_window_length=25,
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mel_window_step=10,
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n_mels=40,
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partial_n_frames=160,
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min_pad_coverage=0.75,
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partial_overlap_ratio=0.5)
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print("Audio Processor Done!")
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speaker_encoder = LSTMSpeakerEncoder(
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n_mels=40, num_layers=3, hidden_size=256, output_size=256)
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speaker_encoder.set_state_dict(paddle.load(args.ge2e_params_path))
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speaker_encoder.eval()
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print("GE2E Done!")
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with open(args.phones_dict, "r") as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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print("vocab_size:", vocab_size)
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odim = fastspeech2_config.n_mels
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model = FastSpeech2(
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idim=vocab_size, odim=odim, **fastspeech2_config["model"])
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model.set_state_dict(
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paddle.load(args.fastspeech2_checkpoint)["main_params"])
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model.eval()
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vocoder = PWGGenerator(**pwg_config["generator_params"])
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vocoder.set_state_dict(paddle.load(args.pwg_checkpoint)["generator_params"])
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vocoder.remove_weight_norm()
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vocoder.eval()
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print("model done!")
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frontend = Frontend(phone_vocab_path=args.phones_dict)
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print("frontend done!")
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stat = np.load(args.fastspeech2_stat)
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mu, std = stat
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mu = paddle.to_tensor(mu)
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std = paddle.to_tensor(std)
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fastspeech2_normalizer = ZScore(mu, std)
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stat = np.load(args.pwg_stat)
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mu, std = stat
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mu = paddle.to_tensor(mu)
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std = paddle.to_tensor(std)
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pwg_normalizer = ZScore(mu, std)
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fastspeech2_inference = FastSpeech2Inference(fastspeech2_normalizer, model)
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fastspeech2_inference.eval()
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pwg_inference = PWGInference(pwg_normalizer, vocoder)
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pwg_inference.eval()
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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input_dir = Path(args.input_dir)
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sentence = args.text
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input_ids = frontend.get_input_ids(sentence, merge_sentences=True)
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phone_ids = input_ids["phone_ids"][0]
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for name in os.listdir(input_dir):
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utt_id = name.split(".")[0]
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ref_audio_path = input_dir / name
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mel_sequences = p.extract_mel_partials(p.preprocess_wav(ref_audio_path))
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# print("mel_sequences: ", mel_sequences.shape)
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with paddle.no_grad():
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spembs = speaker_encoder.embed_utterance(
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paddle.to_tensor(mel_sequences))
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# print("spembs shape: ", spembs.shape)
|
||||
|
||||
with paddle.no_grad():
|
||||
wav = pwg_inference(fastspeech2_inference(phone_ids, spembs=spembs))
|
||||
|
||||
sf.write(
|
||||
str(output_dir / (utt_id + ".wav")),
|
||||
wav.numpy(),
|
||||
samplerate=fastspeech2_config.fs)
|
||||
print(f"{utt_id} done!")
|
||||
# Randomly generate numbers of 0 ~ 0.2, 256 is the dim of spembs
|
||||
random_spembs = np.random.rand(256) * 0.2
|
||||
random_spembs = paddle.to_tensor(random_spembs)
|
||||
utt_id = "random_spembs"
|
||||
with paddle.no_grad():
|
||||
wav = pwg_inference(fastspeech2_inference(phone_ids, spembs=spembs))
|
||||
sf.write(
|
||||
str(output_dir / (utt_id + ".wav")),
|
||||
wav.numpy(),
|
||||
samplerate=fastspeech2_config.fs)
|
||||
print(f"{utt_id} done!")
|
||||
|
||||
|
||||
def main():
|
||||
# parse args and config and redirect to train_sp
|
||||
parser = argparse.ArgumentParser(description="")
|
||||
parser.add_argument(
|
||||
"--fastspeech2-config", type=str, help="fastspeech2 config file.")
|
||||
parser.add_argument(
|
||||
"--fastspeech2-checkpoint",
|
||||
type=str,
|
||||
help="fastspeech2 checkpoint to load.")
|
||||
parser.add_argument(
|
||||
"--fastspeech2-stat",
|
||||
type=str,
|
||||
help="mean and standard deviation used to normalize spectrogram when training fastspeech2."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--pwg-config", type=str, help="parallel wavegan config file.")
|
||||
parser.add_argument(
|
||||
"--pwg-checkpoint",
|
||||
type=str,
|
||||
help="parallel wavegan generator parameters to load.")
|
||||
parser.add_argument(
|
||||
"--pwg-stat",
|
||||
type=str,
|
||||
help="mean and standard deviation used to normalize spectrogram when training parallel wavegan."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--phones-dict",
|
||||
type=str,
|
||||
default="phone_id_map.txt",
|
||||
help="phone vocabulary file.")
|
||||
parser.add_argument(
|
||||
"--text",
|
||||
type=str,
|
||||
default="每当你觉得，想要批评什么人的时候，你切要记着，这个世界上的人，并非都具备你禀有的条件。",
|
||||
help="text to synthesize, a line")
|
||||
|
||||
parser.add_argument(
|
||||
"--ge2e_params_path", type=str, help="ge2e params path.")
|
||||
|
||||
parser.add_argument(
|
||||
"--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
|
||||
|
||||
parser.add_argument(
|
||||
"--input-dir",
|
||||
type=str,
|
||||
help="input dir of *.wav, the sample rate will be resample to 16k.")
|
||||
parser.add_argument("--output-dir", type=str, help="output dir.")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.ngpu == 0:
|
||||
paddle.set_device("cpu")
|
||||
elif args.ngpu > 0:
|
||||
paddle.set_device("gpu")
|
||||
else:
|
||||
print("ngpu should >= 0 !")
|
||||
|
||||
with open(args.fastspeech2_config) as f:
|
||||
fastspeech2_config = CfgNode(yaml.safe_load(f))
|
||||
with open(args.pwg_config) as f:
|
||||
pwg_config = CfgNode(yaml.safe_load(f))
|
||||
|
||||
print("========Args========")
|
||||
print(yaml.safe_dump(vars(args)))
|
||||
print("========Config========")
|
||||
print(fastspeech2_config)
|
||||
print(pwg_config)
|
||||
|
||||
voice_cloning(args, fastspeech2_config, pwg_config)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,13 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
@ -0,0 +1,13 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
Loading…
Reference in new issue