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CatalogModelsTTS DE Multi-Speaker FastPitch HiFiGAN

TTS DE Multi-Speaker FastPitch HiFiGAN

Logo for TTS DE Multi-Speaker FastPitch HiFiGAN
Description
This collection includes two German models: FastPitch trained on the HUI-Audio-Corpus-German clean dataset where the 5-largest amount of speakers are selected and balanced; HiFiGAN is trained on mel-spectrograms predicted by the Multi-speaker FastPitch.
Publisher
NVIDIA
Latest Version
1.11.0
Modified
April 4, 2023
Size
498.89 MB

Model Overview

This collection contains two models:

  1. Multi-speaker FastPitch (around 50M parameters) trained on the HUI-Audio-Corpus-German [1] clean dataset. We selected 5 speakers who have the 5-largest amount of data and balanced training data across speakers (around 20 hours per speaker).
  2. HiFiGAN trained on mel-spectrograms predicted by the Multi-speaker 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

Datasets

  • FastPitch: the model is trained from scratch on the clean subset of HUI-Audio-Corpus-German [1] dataset sampled at 44100Hz. The clean subset includes 118 speakers but only 5 of them have at least 20 hours data. We selected 5 speakers (3 males and 2 females) and balanced training data across 5 speakers with around 20 hours for each speaker.
  • HiFiGAN: the model is finetuned from the multi-speaker English HiFiGAN checkpoint 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. In order to generate spectrogram specific to a particular speaker you will need to provide a speaker ID to FastPitch. The speaker IDs are predefined as {53, 36, 37, 62, 34}.

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_de_fastpitch_multispeaker_5")

# Load Vocoder
from nemo.collections.tts.models import HifiGanModel
model = HifiGanModel.from_pretrained(model_name="tts_de_hui_hifigan_ft_fastpitch_multispeaker_5")

# Generate audio
import soundfile as sf
parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.")
speaker_id = 53
spectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=10)
audio = model.convert_spectrogram_to_audio(spec=spectrogram)

# Save the audio to disk in a file called speech.wav
sf.write("speech.wav", audio.to('cpu').numpy(), 44100)

Input

This model accepts batches of text and speaker ID.

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.

References

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