Piper TTS: pt_BR-faber-medium
Medium-size Brazilian Portuguese male voice.
Model Details
| Field | Value |
|---|---|
| Architecture | VITS (end-to-end) |
| Format | ONNX |
| Language | Portuguese (Brazil) |
| Gender | Male |
| Model Size | medium (~63 MB ONNX, ~15M params) |
| Sample Rate | 22050 Hz |
| License | CC0 (Public Domain) |
Note: Piper uses the terms "medium", "high", etc. to refer to model size, not output quality. Medium models (~63 MB, ~15M params) and high models (~114 MB, ~28M params) both produce 22.05 kHz audio.
Usage
With piper-tts (GPL)
from piper import PiperVoice
voice = PiperVoice.load("model.onnx")
for chunk in voice.synthesize("Hello, this is a test."):
# chunk.audio_float_array contains float32 audio
pass
Standalone ONNX (MIT — no piper-tts dependency)
Requires espeak-ng installed (brew install espeak-ng / apt install espeak-ng).
import json, subprocess, numpy as np, onnxruntime as ort, soundfile as sf
from huggingface_hub import hf_hub_download
model_id = "Trelis/piper-pt-br-faber-medium"
onnx_path = hf_hub_download(model_id, "model.onnx")
config_path = hf_hub_download(model_id, "model.onnx.json")
with open(config_path) as f:
config = json.load(f)
session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
phoneme_id_map = config["phoneme_id_map"]
espeak_voice = config["espeak"]["voice"]
def phonemize(text, voice):
out = subprocess.run(
["espeak-ng", "-v", voice, "-q", "--ipa=2", "-x", text],
capture_output=True, text=True,
).stdout.strip()
return [list(line.replace("_", " ")) for line in out.split("\n") if line.strip()]
def to_ids(phonemes, pmap):
ids = [pmap["^"][0], pmap["_"][0]]
for p in phonemes:
if p in pmap:
ids.extend(pmap[p])
ids.append(pmap["_"][0])
ids.append(pmap["$"][0])
return ids
text = "Hello, this is a test."
audio_chunks = []
for sentence in phonemize(text, espeak_voice):
ids = to_ids(sentence, phoneme_id_map)
if len(ids) < 3:
continue
audio = session.run(None, {
"input": np.array([ids], dtype=np.int64),
"input_lengths": np.array([len(ids)], dtype=np.int64),
"scales": np.array([
config["inference"]["noise_scale"],
config["inference"]["length_scale"],
config["inference"]["noise_w"],
], dtype=np.float32),
})[0]
audio_chunks.append(audio.squeeze())
audio = np.concatenate(audio_chunks).astype(np.float32)
sf.write("output.wav", audio, config["audio"]["sample_rate"])
Fine-tuning
You can fine-tune this model on your own voice data using Trelis Studio. Piper models can be trained on custom datasets to create personalized voices.
Attribution
Trained on OHF Voice Datasets. Fine-tuned from lessac medium.
Re-hosted from rhasspy/piper-voices.
Original voice: pt_BR-faber-medium