Piper TTS: ru_RU-irina-medium
Medium-size Russian female voice.
Model Details
| Field | Value |
|---|---|
| Architecture | VITS (end-to-end) |
| Format | ONNX |
| Language | Russian (Russia) |
| Gender | Female |
| Model Size | medium (~63 MB ONNX, ~15M params) |
| Sample Rate | 22050 Hz |
| License | See source (RHVoice) |
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-ru-ru-irina-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 data from RHVoice, speaker Irina. Fine-tuned from lessac medium.
Re-hosted from rhasspy/piper-voices.
Original voice: ru_RU-irina-medium