This collection contains two models:
FastPitch  is a non-autoregressive model for mel-spectrogram generation based on FastSpeech , conditioned on fundamental frequency contours. It uses an external Tacotron 2  model trained on LJSpeech-1.1 to extract training alignments, and estimate durations of input symbols. NeMo implemetation leverages a novel alignment framework  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  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.
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
No performance information available at this time.
The model is available for use in the NeMo toolkit , 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.
# 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')
This model accepts batches of texts.
This model generates mel spectrograms.
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.
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