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CatalogModelsTTS Zh Fastpitch HifiGan SFSpeech

TTS Zh Fastpitch HifiGan SFSpeech

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Description

This model card includes two Mandarin Chinese models: 1) FastPitch Mel-spectrogram generator trained on SF Chinese/English Bilingual Speech dataset; 2) HiFiGAN vocoder trained on Mel-spectrograms predicted by the FastPitch.

Publisher

NVIDIA

Use Case

Text To Speech

Framework

PyTorch

Latest Version

1.15.0

Modified

February 3, 2023

Size

498.32 MB

Model Overview

This collection contains two models:

  1. Single-speaker FastPitch (around 50M parameters) trained on SF Chinese/English Bilingual Speech dataset [1].
  2. HiFiGAN trained on Mel-spectrograms predicted by the FastPitch in (1).

Model Architecture

FastPitch [2] is a non-autoregressive model for mel-spectrogram generation based on FastSpeech [3], conditioned on fundamental frequency contours. It uses an external Tacotron 2 [4] model trained on LJSpeech-1.1 to extract training alignments, and estimate durations of input symbols. NeMo implemetation leverages a novel alignment framework [5] to simplify the alignment learning in TTS models. For more informaiton on training a FastPitch model, please refer to NeMo tutorial FastPitch_MixerTTS_Training.

HiFiGAN [6] is a generative adversarial network (GAN) model that generates audios from mel-spectrograms. The generator uses transposed convolutions to upsample mel-spectrograms to audios. For more details about HiFiGAN, please refer to its original paper. NeMo re-implementation of HiFiGAN can be found here.

Training

You can follow the NeMo Chinese TTS training tutorial for details: https://github.com/NVIDIA/NeMo/blob/r1.14.0/tutorials/tts/FastPitch_ChineseTTS_Training.ipynb

Datasets

  • FastPitch: the model is trained from scratch on SF Chinese/English Bilingual Speech dataset [1] sampled at 22050 Hz. The dataset contains about 2,740 bilingual audio samples of a single female speaker and their corresponding text transcripts, each of them is an audio of around 5-6 seconds and have a total length of approximately 4.5 hours.
  • HiFiGAN: the model is finetuned from the tts_en_lj_hifigan_ft_mixerttsx.nemo in TTS En LJ HiFi-GAN by the mel-spectrograms generated from the FastPitch model above.

Performance

No performance information available at this time.

How to Use this Model

The model is available for use in the NeMo toolkit [7], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

NOTE: For best results you should use the vocoder (HiFiGAN) checkpoint in this model card along with the mel spectrogram generator (FastPitch) checkpoint.

Automatically load the model from NGC

# Load spectrogram generator
from nemo.collections.tts.models import FastPitchModel
spec_generator = FastPitchModel.from_pretrained(model_name="tts_zh_fastpitch_sfspeech")

# Load Vocoder
from nemo.collections.tts.models import HifiGanModel
model = HifiGanModel.from_pretrained(model_name="tts_zh_hifigan_sfspeech")

# Generate audio
import soundfile as sf
import torch
with torch.no_grad():
    parsed = spec_generator.parse("这些新一代的CPU不只效能惊人。")
    spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
    audio = model.convert_spectrogram_to_audio(spec=spectrogram)
    if isinstance(audio, torch.Tensor):
        audio = audio.to('cpu').numpy()

# Save the audio to disk in a file called speech.wav
sf.write("speech.wav", audio.T, 22050, format='WAV')

Input

This model accepts batches of texts.

Output

This model generates mel spectrograms.

Limitations

Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.

Versions

  • 1.15 (latest): Disambiguate polyphones with augmented dict and Jieba segmenter.
  • 1.14: Initial models.

References

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