Model Overview
This collection contains two models:
FastPitch (around 50M parameters) trained on OpenSLR neutral German dataset with over 23 hours of German speech and 1 speaker.
HiFi-GAN trained on mel spectrograms produced by the FastPitch model in (1).
Model Architecture
FastPitch
FastPitch is non-autoregressive model for mel-spectrogram generation based on FastSpeech, conditioned on fundamental frequency contours. For more details about model, please refer to the original paper. Original FastPitch model uses an external Tacotron 2 model trained on LJSpeech-1.1 to extract training alignments and estimate durations of input symbols. This implementation of FastPitch is based on Deep Learning Examples, which uses an alignment mechanism proposed in RAD-TTS and extended in TTS Aligner.
For more information on training a basic FastPitch model, please refer to FastPitch_MixerTTS_Training.ipynb tutorial.
HiFi-GAN
HiFi-GAN is a generative adversarial network (GAN) model that generates audio from mel spectrograms. The generator uses transposed convolutions to upsample mel spectrograms to audio. For more details about the model, please refer to the original paper. NeMo re-implementation of HiFi-GAN can be found here.
Training
Datasets
FastPitch: This model is trained from scratch on one male speaker named Thorsten Müller from OpenSLR - German Neutral-TTS dataset sampled at 22050Hz. Link here.
HiFi-GAN: This model is derived after finetuning TTS Vocoder Hifigan v1.0.0rc1 (pretrained on English dataset) on predicted mel spectrograms from FastPitch above.
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.
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_de_fastpitch")
# Load Vocoder
from nemo.collections.tts.models import HifiGanModel
model = HifiGanModel.from_pretrained(model_name="tts_de_openslr_hifigan_ft_fastpitch")
# Generate audio
import soundfile as sf
parsed = spec_generator.parse("Sie können Ihren Satz hier eingeben, um Nemo dazu zu bringen, Sprache zu produzieren.")
spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
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(), 22050)
Input
This model accepts batches of text.
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. This checkpoint only works well with vocoders that were trained on 22050Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding.
Versions
- 1.15.0 (latest): This version applies grapheme-based tokenizer with grapheme-only symbol set, where the capitalization of each word is preserved. It includes FastPitch and HifiGan models trained on Thorsten's Neutral Datasets 21.02 and 22.10, respectively. There are four model checkpoints available.
- 1.10.0: This version applies phoneme-based tokenizer with the symbol set mixed with both phonemes and graphemes on Thorsten's Neural 21.02 dataset, where sentenc-level grapheme sequence and phoneme sequence for each transcript are considered as valid inputs. The version of FastPitch 1.1 without energy conditioning, see details here.
References
[1] FastPitch: https://arxiv.org/abs/2006.06873
[2] One TTS Alignment To Rule Them All: https://arxiv.org/abs/2108.10447
[3] HiFi-GAN paper: https://arxiv.org/abs/2010.05646
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