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TTS En Multispeaker FastPitch HiFiGAN

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Description

This collection contains two models: 1) Multi-speaker FastPitch (around 50M parameters) trained on HiFiTTS with over 291.6 hours of english speech and 10 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

1.10.0

Modified

August 9, 2022

Size

521.09 MB

Model Overview

This collection contains two models:

  1. Multi-speaker FastPitch (around 50M parameters) trained on HiFiTTS with over 291.6 hours of english speech and 10 speakers.

  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.

Training

Datasets

This model is trained on HiFiTTS sampled at 44100Hz, and has been tested on generating female and male English voices with an American accent.

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 1 to 20. For security purposes the model generates spectrograms for synthetic speakers generated by interpolating original HiFiTTS speakers.

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("tts_en_fastpitch_multispeaker")

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

# Generate audio
import soundfile as sf
parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.")
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
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

[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

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