PaddleSpeech/docs/source/tts/quick_start.md

Quick Start of Text-to-Speech

The examples in PaddleSpeech are mainly classified by datasets, the TTS datasets we mainly used are:

• CSMCS (Mandarin single speaker)
• AISHELL3 (Mandarin multiple speaker)
• LJSpeech (English single speaker)
• VCTK (English multiple speaker)

The models in PaddleSpeech TTS have the following mapping relationship:

• tts0 - Tactron2
• tts1 - TransformerTTS
• tts2 - SpeedySpeech
• tts3 - FastSpeech2
• voc0 - WaveFlow
• voc1 - Parallel WaveGAN
• voc2 - MelGAN
• voc3 - MultiBand MelGAN
• vc0 - Tactron2 Voice Clone with GE2E
• vc1 - FastSpeech2 Voice Clone with GE2E

Quick Start

Let's take a FastSpeech2 + Parallel WaveGAN with CSMSC dataset for instance. (./examples/csmsc/)(https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc)

Train Parallel WaveGAN with CSMSC

• Go to directory

  cd examples/csmsc/voc1
  

• Source env

  source path.sh
  

  Must do this before you start to do anything. Set MAIN_ROOT as project dir. Using parallelwave_gan model as MODEL.

• Main entrypoint

  bash run.sh
  

  This is just a demo, please make sure source data have been prepared well and every step works well before next step.

Train FastSpeech2 with CSMSC

• Go to directory

  cd examples/csmsc/tts3
  

• Source env

  source path.sh
  

  Must do this before you start to do anything. Set MAIN_ROOT as project dir. Using fastspeech2 model as MODEL.
• Main entrypoint

  bash run.sh
  

  This is just a demo, please make sure source data have been prepared well and every step works well before next step.

The steps in run.sh mainly include:

• source path.
• preprocess the dataset,
• train the model.
• synthesize waveform from metadata.jsonl.
• synthesize waveform from text file. (in acoustic models)
• inference using static model. (optional)

For more details , you can see README.md in examples.

Pipeline of TTS

This section shows how to use pretrained models provided by TTS and make inference with them.

Pretrained models in TTS are provided in a archive. Extract it to get a folder like this: Acoustic Models:

checkpoint_name
├── default.yaml
├── snapshot_iter_*.pdz
├── speech_stats.npy
├── phone_id_map.txt
├── spk_id_map.txt (optimal)
└── tone_id_map.txt (optimal)

Vocoders:

checkpoint_name
├── default.yaml  
├── snapshot_iter_*.pdz
└── stats.npy  

• default.yaml stores the config used to train the model.
• snapshot_iter_*.pdz is the chechpoint file, where * is the steps it has been trained.
• *_stats.npy is the stats file of feature if it has been normalized before training.
• phone_id_map.txt is the map of phonemes to phoneme_ids.
• tone_id_map.txt is the map of tones to tones_ids, when you split tones and phones before training acoustic models. (for example in our csmsc/speedyspeech example)
• spk_id_map.txt is the map of spkeaker to spk_ids in multi-spk acoustic models. (for example in our aishell3/fastspeech2 example)

The example code below shows how to use the models for prediction.

Acoustic Models (text to spectrogram)

The code below show how to use a FastSpeech2 model. After loading the pretrained model, use it and normalizer object to construct a prediction object，then use fastspeech2_inferencet(phone_ids) to generate spectrograms, which can be further used to synthesize raw audio with a vocoder.

from pathlib import Path
import numpy as np
import paddle
import yaml
from yacs.config import CfgNode
from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
from paddlespeech.t2s.models.fastspeech2 import FastSpeech2Inference
from paddlespeech.t2s.modules.normalizer import ZScore
# examples/fastspeech2/baker/frontend.py
from frontend import Frontend

# load the pretrained model
checkpoint_dir = Path("fastspeech2_nosil_baker_ckpt_0.4")
with open(checkpoint_dir / "phone_id_map.txt", "r") as f:
    phn_id = [line.strip().split() for line in f.readlines()]
vocab_size = len(phn_id)
with open(checkpoint_dir / "default.yaml") as f:
    fastspeech2_config = CfgNode(yaml.safe_load(f))
odim = fastspeech2_config.n_mels
model = FastSpeech2(
    idim=vocab_size, odim=odim, **fastspeech2_config["model"])
model.set_state_dict(
    paddle.load(args.fastspeech2_checkpoint)["main_params"])
model.eval()

# load stats file
stat = np.load(checkpoint_dir / "speech_stats.npy")
mu, std = stat
mu = paddle.to_tensor(mu)
std = paddle.to_tensor(std)
fastspeech2_normalizer = ZScore(mu, std)

# construct a prediction object
fastspeech2_inference = FastSpeech2Inference(fastspeech2_normalizer, model)

# load Chinese Frontend
frontend = Frontend(checkpoint_dir / "phone_id_map.txt")

# text to spectrogram
sentence = "你好吗？"
input_ids = frontend.get_input_ids(sentence, merge_sentences=True)
phone_ids = input_ids["phone_ids"]
flags = 0
# The output of Chinese text frontend is segmented
for part_phone_ids in phone_ids:
    with paddle.no_grad():
        temp_mel = fastspeech2_inference(part_phone_ids)
        if flags == 0:
            mel = temp_mel
            flags = 1
        else:
            mel = paddle.concat([mel, temp_mel])

Vocoder (spectrogram to wave)

The code below show how to use a Parallel WaveGAN model. Like the example above, after loading the pretrained model, use it and normalizer object to construct a prediction object，then use pwg_inference(mel) to generate raw audio (in wav format).

from pathlib import Path
import numpy as np
import paddle
import soundfile as sf
import yaml
from yacs.config import CfgNode
from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
from paddlespeech.t2s.models.parallel_wavegan import PWGInference
from paddlespeech.t2s.modules.normalizer import ZScore

# load the pretrained model
checkpoint_dir = Path("parallel_wavegan_baker_ckpt_0.4")
with open(checkpoint_dir / "pwg_default.yaml") as f:
    pwg_config = CfgNode(yaml.safe_load(f))
vocoder = PWGGenerator(**pwg_config["generator_params"])
vocoder.set_state_dict(paddle.load(args.pwg_params))
vocoder.remove_weight_norm()
vocoder.eval()

# load stats file
stat = np.load(checkpoint_dir / "pwg_stats.npy")
mu, std = stat
mu = paddle.to_tensor(mu)
std = paddle.to_tensor(std)
pwg_normalizer = ZScore(mu, std)

# construct a prediction object
pwg_inference = PWGInference(pwg_normalizer, vocoder)

# spectrogram to wave
wav = pwg_inference(mel)
sf.write(
        audio_path,
        wav.numpy(),
        samplerate=fastspeech2_config.fs)