fix some librosa bugs, test=tts

pull/1436/head
TianYuan 3 years ago
parent 30085ac229
commit 7dc1f2daa3

@ -34,7 +34,7 @@ Language Model | Training Data | Token-based | Size | Descriptions
Model Type | Dataset| Example Link | Pretrained Models|Static Models|Size (static)
:-------------:| :------------:| :-----: | :-----:| :-----:| :-----:
Tacotron2|LJSpeech|[tacotron2-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts0)|[tacotron2_ljspeech_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.2.0.zip)|||
Tacotron2|CSMSC|[tacotron2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts0)|[tacotron2_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip)|[tacotron2_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_static_0.2.0.zip)|94.95MB|
Tacotron2|CSMSC|[tacotron2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts0)|[tacotron2_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip)|[tacotron2_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_static_0.2.0.zip)|103MB|
TransformerTTS| LJSpeech| [transformer-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts1)|[transformer_tts_ljspeech_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/transformer_tts/transformer_tts_ljspeech_ckpt_0.4.zip)|||
SpeedySpeech| CSMSC | [speedyspeech-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts2) |[speedyspeech_nosil_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_ckpt_0.5.zip)|[speedyspeech_nosil_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip)|12MB|
FastSpeech2| CSMSC |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts3)|[fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip)|[fastspeech2_nosil_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip)|157MB|
@ -54,6 +54,8 @@ Parallel WaveGAN| VCTK |[PWGAN-vctk](https://github.com/PaddlePaddle/PaddleSpeec
|Multi Band MelGAN | CSMSC |[MB MelGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc3) | [mb_melgan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip) <br>[mb_melgan_baker_finetune_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_finetune_ckpt_0.5.zip)|[mb_melgan_csmsc_static_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_static_0.1.1.zip) |8.2MB|
Style MelGAN | CSMSC |[Style MelGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc4)|[style_melgan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/style_melgan/style_melgan_csmsc_ckpt_0.1.1.zip)| | |
HiFiGAN | CSMSC |[HiFiGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc5)|[hifigan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip)|[hifigan_csmsc_static_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_static_0.1.1.zip)|50MB|
WaveRNN | CSMSC |[WaveRNN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc6)|[wavernn_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip)|[wavernn_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_static_0.2.0.zip)|18MB|
### Voice Cloning
Model Type | Dataset| Example Link | Pretrained Models

@ -34,7 +34,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_csmsc \
--am=tacotron2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
@ -56,7 +56,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_csmsc \
--am=tacotron2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
@ -77,7 +77,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_csmsc \
--am=tacotron2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
@ -91,3 +91,24 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
--inference_dir=${train_output_path}/inference \
--phones_dict=dump/phone_id_map.txt
fi
# wavernn
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
echo "in wavernn syn_e2e"
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=tacotron2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=wavernn_csmsc \
--voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \
--voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \
--voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \
--lang=zh \
--text=${BIN_DIR}/../sentences.txt \
--output_dir=${train_output_path}/test_e2e \
--phones_dict=dump/phone_id_map.txt \
--inference_dir=${train_output_path}/inference
fi

@ -92,3 +92,26 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
--phones_dict=dump/phone_id_map.txt \
--tones_dict=dump/tone_id_map.txt
fi
# wavernn
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
echo "in wavernn syn_e2e"
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=speedyspeech_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/feats_stats.npy \
--voc=wavernn_csmsc \
--voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \
--voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \
--voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \
--lang=zh \
--text=${BIN_DIR}/../sentences.txt \
--output_dir=${train_output_path}/test_e2e \
--phones_dict=dump/phone_id_map.txt \
--tones_dict=dump/tone_id_map.txt \
--inference_dir=${train_output_path}/inference
fi

@ -102,9 +102,9 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=wavernn_csmsc \
--voc_config=wavernn_test/default.yaml \
--voc_ckpt=wavernn_test/snapshot_iter_5000.pdz \
--voc_stat=wavernn_test/feats_stats.npy \
--voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \
--voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \
--voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \
--lang=zh \
--text=${BIN_DIR}/../sentences.txt \
--output_dir=${train_output_path}/test_e2e \

@ -415,11 +415,11 @@ def mfcc(x,
**kwargs)
# librosa mfcc:
spect = librosa.feature.melspectrogram(x,sr=16000,n_fft=512,
spect = librosa.feature.melspectrogram(y=x,sr=16000,n_fft=512,
win_length=512,
hop_length=320,
n_mels=64, fmin=50)
b = librosa.feature.mfcc(x,
b = librosa.feature.mfcc(y=x,
sr=16000,
S=spect,
n_mfcc=20,

@ -67,7 +67,7 @@ def istft(x, n_shift, win_length=None, window="hann", center=True):
x = np.stack(
[
librosa.istft(
y=x[:, ch].T, # [Time, Freq] -> [Freq, Time]
stft_matrix=x[:, ch].T, # [Time, Freq] -> [Freq, Time]
hop_length=n_shift,
win_length=win_length,
window=window,

@ -53,8 +53,8 @@ class AudioProcessor(object):
def _create_mel_filter(self):
mel_filter = librosa.filters.mel(
self.sample_rate,
self.n_fft,
sr=self.sample_rate,
n_fft=self.n_fft,
n_mels=self.n_mels,
fmin=self.fmin,
fmax=self.fmax)

@ -38,7 +38,7 @@ class AudioSegmentDataset(Dataset):
def __getitem__(self, i):
fpath = self.file_paths[i]
y, sr = librosa.load(fpath, self.sr)
y, sr = librosa.load(fpath, sr=self.sr)
y, _ = librosa.effects.trim(y, top_db=self.top_db)
y = librosa.util.normalize(y)
y = y.astype(np.float32)
@ -70,7 +70,7 @@ class AudioDataset(Dataset):
def __getitem__(self, i):
fpath = self.file_paths[i]
y, sr = librosa.load(fpath, self.sr)
y, sr = librosa.load(fpath, sr=self.sr)
y, _ = librosa.effects.trim(y, top_db=self.top_db)
y = librosa.util.normalize(y)
y = y.astype(np.float32)

@ -31,7 +31,7 @@ from paddlespeech.t2s.models.wavernn import WaveRNN
def main():
parser = argparse.ArgumentParser(description="Synthesize with WaveRNN.")
parser.add_argument("--config", type=str, help="GANVocoder config file.")
parser.add_argument("--config", type=str, help="Vocoder config file.")
parser.add_argument("--checkpoint", type=str, help="snapshot to load.")
parser.add_argument("--test-metadata", type=str, help="dev data.")
parser.add_argument("--output-dir", type=str, help="output dir.")

@ -179,7 +179,7 @@ def train_sp(args, config):
def main():
# parse args and config and redirect to train_sp
parser = argparse.ArgumentParser(description="Train a HiFiGAN model.")
parser = argparse.ArgumentParser(description="Train a WaveRNN model.")
parser.add_argument(
"--config", type=str, help="config file to overwrite default config.")
parser.add_argument("--train-metadata", type=str, help="training data.")

Loading…
Cancel
Save