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CatalogModelsTTS Es Multispeaker FastPitch HiFiGAN

TTS Es Multispeaker FastPitch HiFiGAN

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Description

This collection contains two models. 1) Multi-speaker 44100Hz FastPitch trained on approximately 20 hours of Latin American Spanish speech from 174 speakers. 2) HiFiGAN trained on mel spectrograms produced by the Multi-speaker FastPitch in (1).

Publisher

NVIDIA

Use Case

Text To Speech

Framework

PyTorch

Latest Version

1

Modified

November 8, 2022

Size

498.96 MB

Model Overview

This collection contains two models:

  1. Multi-speaker 44100Hz FastPitch (around 50M parameters) trained on approximately 20 hours of Latin American Spanish speech from 174 speakers (1 to 10 minutes of audio per speaker) spread across 6 locales (es-AR, es-CL, es-CO, es-PE, es-PR, es-VE) from OpenSLR.

  2. HiFiGAN trained on mel spectrograms produced by the Multi-speaker FastPitch in (1).

Model Architecture

FastPitch [1] is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. FastPitch is based on a fully-parallel Transformer architecture, with much higher real-time factor than Tacotron2 for mel-spectrogram synthesis of a typical utterance. Additionally it uses unsupervised speech-text aligner [2].

HiFiGAN [3], a generative adversarial network (GAN) model that generates audio from mel spectrograms produced by the Multi-speaker FastPitch in (1). The generator uses transposed convolutions to upsample mel spectrograms to audio.

Dataset

The original OpenSLR dataset contains approximately 38 hours of audio sampled at 48000Hz. For training we downsample to 44100Hz, and trim the silence from the beginning and end of each file (totaling about 18 hours of silence), reducing the dataset size from 38 hours to 20 hours.

https://www.openslr.org/61

https://www.openslr.org/71

https://www.openslr.org/72

https://www.openslr.org/73

https://www.openslr.org/74

https://www.openslr.org/75

https://research.google/pubs/pub49150/

Performance

No performance information available at this time.

How to Use this Model

The model is available for use in the NeMo toolkit [4], 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 speaker ID to FastPitch. The speaker IDs go from 0 to 173.

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

model_name = "tts_es_multispeaker_fastpitchhifigan"

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

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

# Generate audio
import soundfile as sf
text = "la ciudad es bellĂ­sima y tan alegre."
parsed = spec_generator.parse(text, normalize=False)
speaker_id = 10
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
sample_rate = 44100
sf.write("speech.wav", audio.to('cpu').numpy(), sample_rate)

Input/Output

The FastPitch model accepts batches of text and speaker IDs and outputs mel spectrograms. The HifiGan model accepts batches of mel spectrograms and outputs audio.

Limitations

This model was trained on a limited amount of publically available speech data. The performance might degrade for speech which includes terms or vernacular that the model has not been trained on. The model might also perform worse on certain locales, regional dialects, or accents.

References

[1] Fastpitch: https://arxiv.org/abs/2006.06873

[2] One TTS Alignment To Rule Them All: https://arxiv.org/abs/2108.10447

[3] HiFiGan paper: https://arxiv.org/abs/2010.05646

[4] NVIDIA NeMo Toolkit: https://github.com/NVIDIA/NeMo

[5] OpenSLR data paper: https://arxiv.org/abs/2010.05646

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