fastspeech2 en ljspeech

Introduction

FastSpeech 2 is a text-to-speech model developed as part of the fairseq S^2 toolkit. It is designed to convert text into audio using a single-speaker female voice. The model is trained on the LJSpeech dataset, which is a commonly used dataset for speech synthesis research.

Architecture

FastSpeech 2 employs a non-autoregressive approach to text-to-speech synthesis, allowing for faster and more efficient generation of speech compared to traditional methods. The model utilizes the fairseq library, which is known for its flexible architecture designed for various sequence-to-sequence tasks.

Training

The model has been trained on the LJSpeech dataset, which consists of high-quality recordings of a single female speaker reading passages from public domain texts. This dataset is widely used for training models in the text-to-speech domain due to its clear and consistent speech patterns.

Guide: Running Locally

To run the FastSpeech 2 model locally, you can use the following Python code snippet:

from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd

models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
    "facebook/fastspeech2-en-ljspeech",
    arg_overrides={"vocoder": "hifigan", "fp16": False}
)
model = models[0]
TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
generator = task.build_generator(model, cfg)

text = "Hello, this is a test run."

sample = TTSHubInterface.get_model_input(task, text)
wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)

ipd.Audio(wav, rate=rate)

This script demonstrates how to load the model from Hugging Face's model hub, process an input text, and generate the corresponding audio.

For optimal performance, consider using a cloud GPU service such as AWS, Google Cloud, or Azure, as these platforms provide powerful resources that can handle the computational demands of running advanced machine learning models.

License

The FastSpeech 2 model is distributed under the licensing terms specified by the authors and contributors of the fairseq library. Users are advised to consult the respective licenses for details on usage, distribution, and modification rights.