dmcartor/spa-speaker_embeddings_dataset

Overview

This dataset contains speaker embeddings generated for Spanish speech synthesis tasks. The embeddings were derived from the facebook/multilingual_librispeech dataset, specifically using the Spanish subset. The dataset is intended for use in speech-to-speech translation and other related speech processing tasks that may benefit from incorporating speaker-specific characteristics.

Dataset Summary

The facebook/multilingual_librispeech dataset is a multilingual dataset derived from Librispeech, consisting of audio recordings in multiple languages, including Spanish. The dataset provides a rich source of diverse speech samples across various speakers, which makes it suitable for training and evaluating speech models, including Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and speaker identification systems.

Key Features of the Spanish Subset:

Audio Format: .opus files, sampled at 16kHz. Duration: Contains approximately 10 hours of audio for the Spanish subset. Speakers: Includes multiple speakers with various demographics and voice characteristics. Usage: The dataset is primarily used for research in multilingual ASR and TTS tasks. Methodology Data Preparation Speaker Selection: I selected a subset of speakers from the Spanish portion of the multilingual_librispeech dataset based on the availability of sufficient audio samples (aiming for around 5 to 10 minutes of speech per speaker). Audio Preprocessing: The audio samples were preprocessed to ensure consistency in sampling rate and quality. Only high-quality samples were used to generate the embeddings to capture clear speaker characteristics. Embedding Generation Model Used: speechbrain/spkrec-xvect-voxceleb This model is a pre-trained x-vector model from SpeechBrain designed for speaker recognition tasks. It generates a 512-dimensional embedding vector for each input audio sample, capturing the unique vocal characteristics of the speaker. Procedure: The selected audio samples were passed through the speechbrain/spkrec-xvect-voxceleb model. Speaker embeddings were generated by encoding the audio samples into 512-dimensional vectors. For each speaker, multiple embeddings were generated, and the mean of these embeddings was computed to obtain a representative embedding for the speaker.

Uncertainty in Embedding Quality

The quality and effectiveness of the generated speaker embeddings are currently uncertain due to several factors:

Mismatch with TTS Model: Initial attempts to use these embeddings with a specific TTS model (SpeechT5) resulted in poor output quality, where the synthesized speech did not adequately reflect the intended speaker characteristics. This suggests a possible mismatch between the embeddings and the model.

Lack of Fine-Tuning: The embeddings were generated using a pre-trained model (speechbrain/spkrec-xvect-voxceleb) that was not specifically fine-tuned for the Spanish language or the particular speakers in the dataset. This might limit the effectiveness of the embeddings in capturing nuanced speaker characteristics.

Data Limitations: While efforts were made to select high-quality audio samples, the dataset's diversity and variability in speaker characteristics may have impacted the consistency of the embeddings. More extensive and varied data might be needed for better results.

Recommendations for Use Experimental Use: These embeddings are suitable for experimental use and could serve as a starting point for more refined models or use cases. Future Fine-Tuning: Consider fine-tuning a TTS model or the speaker embedding model on a larger, more representative dataset for the target language to achieve better results. Feedback and Contributions: Users are encouraged to provide feedback on the embeddings' effectiveness and contribute improvements or new embeddings based on this dataset. How to Use the Embeddings To use these embeddings, download the .npy files and load them in your Python environment using NumPy:

python

import numpy as np

# Load speaker embeddings
male_speaker_embeddings = np.load("11670_embeddings.npy")
female_speaker_embeddings = np.load("2308_embeddings.npy")

License and Acknowledgments

The facebook/multilingual_librispeech dataset is used under its respective license. The embeddings were generated using the speechbrain/spkrec-xvect-voxceleb model from the SpeechBrain library. Please adhere to the licensing terms of the original dataset and models when using these embeddings.