This collection contains two models:
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.
HiFiGAN trained on mel spectrograms produced by the Multi-speaker FastPitch in (1).
FastPitch  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 . It is trained used a mixed text representation of graphemes and IPA phonemes .
HiFiGAN , 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.
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.
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.
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.
import soundfile as sf from nemo.collections.tts.models import FastPitchModel from nemo.collections.tts.models import HifiGanModel fastpitch_name = "tts_es_fastpitch_multispeaker" hifigan_name = "tts_es_hifigan_ft_fastpitch_multispeaker" # Load spectrogram generator spec_generator = FastPitchModel.from_pretrained(fastpitch_name) # Load Vocoder model = HifiGanModel.from_pretrained(hifigan_name) # Generate audio text = "Escribe tu texto aquí." # Optionally, provide custom IPA input escaped with | # text = "|e s k ɾ ˈ i β e| tu |t ˈ e k s t o| aquí." parsed = spec_generator.parse(text, normalize=False) speaker = 5 spectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=speaker) audio = model.convert_spectrogram_to_audio(spec=spectrogram) audio = audio.detach().cpu().numpy() # Save the audio to disk in a file called speech.wav sample_rate = 44100 sf.write("speech.wav", audio, sample_rate)
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.
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.
1.15.0 (current): Added IPA support. Contains built in pronunciation dictionary and accepts mixed inputs of graphemes and IPA phonemes. Trained with speaker level pitch normalization. 1.14.0: The original version released with NeMo 1.14.0 which supports Spanish grapheme input.
 Fastpitch: https://arxiv.org/abs/2006.06873
 One TTS Alignment To Rule Them All: https://arxiv.org/abs/2108.10447
 Mixed representation training: https://arxiv.org/abs/1811.07240
 HiFiGan paper: https://arxiv.org/abs/2010.05646
 NVIDIA NeMo Toolkit: https://github.com/NVIDIA/NeMo
 OpenSLR data paper: https://arxiv.org/abs/2010.05646
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