HiFiGAN-Basque-Maider-Antton

Basque Unit-HiFiGAN Vocoder (Voices: Maider & Antton)

Model Summary

This repository provides a Unit-HiFiGAN vocoder trained to synthesize high-fidelity Basque speech from discrete HuBERT-derived unit sequences. The model supports two speaker identities, Maider and Antton, using learned speaker-conditioning embeddings. It is compatible with HuBERT features extracted from layer 9 and clustered using a KMeans (k=1000) quantizer.

The vocoder is designed for unit-based text-to-speech, voice conversion, and speech synthesis research in Basque. It reconstructs waveform audio from sequences of discrete unit IDs and optional speaker embeddings.

Key Features

Voices: Maider and Antton

Architecture: Unit-HiFiGAN (SpeechBrain implementation)

Input: Discrete HuBERT units (1D sequence of cluster IDs)

Output: 16 kHz Basque speech signal

Speaker conditioning: Single-speaker or multi-speaker inference via speaker embeddings

Compatible encoders: Basque-finetuned HuBERT (layer 9 hidden states → KMeans)

Use cases: Basque TTS research, unit-based synthesis, voice conversion, controllable speaker identity

How to Use

Install speechbrain:

pip install speechbrain

Below is a minimal inference example that replicates the expected workflow:

import torch
import torchaudio
import joblib
import numpy as np
from transformers import Wav2Vec2Processor, HubertModel
from speechbrain.inference.vocoders import UnitHIFIGAN
from huggingface_hub import hf_hub_download

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
SR = 16000

# 1. Load HuBERT
processor = Wav2Vec2Processor.from_pretrained("Ansu/HiFiGAN-Basque-Maider-Antton")
hubert = HubertModel.from_pretrained("Ansu/HiFiGAN-Basque-Maider-Antton").to(DEVICE).eval()

# 2. Load KMeans
kmeans_path = hf_hub_download("Ansu/HiFiGAN-Basque-Maider-Antton", "kmeans/basque_hubert_k1000_L9.pt")
kmeans = joblib.load(kmeans_path)

# 3. Load vocoder
vocoder = UnitHIFIGAN.from_hparams(
    source="your-vocoder-repo",
    run_opts={"device": DEVICE}
).eval()

# 4. Load audio
wav, sr = torchaudio.load("example.wav")
wav = torchaudio.functional.resample(wav, sr, SR)

# 5. HuBERT → units
inputs = processor(wav, sampling_rate=SR, return_tensors="pt")
inputs["input_values"] = inputs["input_values"].to(DEVICE)

with torch.no_grad():
    hidden = hubert(**inputs, output_hidden_states=True).hidden_states[9]

features = hidden.squeeze(0).cpu().numpy()
unit_ids = kmeans.predict(features)
units = torch.LongTensor(unit_ids).unsqueeze(0).unsqueeze(-1).to(DEVICE)

# 6. Speaker embedding (Maider or Antton)
spk_emb = torch.FloatTensor(
    np.load("speaker_embeddings/maider.npy")
).unsqueeze(0).to(DEVICE)

# 7. Vocoder decode
with torch.no_grad():
    wav_out = vocoder.decode_batch(units, spk_emb=spk_emb)

torchaudio.save("output_maider.wav", wav_out.cpu(), SR)
print("Saved: output_maider.wav")

Acknowledgements

This work has been partially supported by the Basque Government (IKER-GAITU project), the Spanish Ministry for Digital Transformation and of Civil Service, and the EU-funded NextGenerationEU Recovery, Transformation and Resilience Plan (ILENIA project, 2022/TL-22/00215335 & ALIA).