rho-tts 1.0.5

Multi-provider text-to-speech library with voice cloning, accent drift detection, and STT validation

rho-tts

Multi-provider text-to-speech library with voice cloning, accent drift detection, and STT validation.

Features

• Multi-provider TTS — Swap between Qwen3-TTS and Chatterbox with a single parameter
• Voice cloning — Clone any voice from a short reference audio sample
• Accent drift detection — ML classifier catches when the generated voice drifts from your target accent
• STT validation — Whisper-based transcription check ensures the model actually said what you asked it to
• Speaker similarity — Cosine similarity scoring between generated and reference voice embeddings
• Audio post-processing — Silence trimming, crossfading, DC offset removal, fade-in/out
• Batch processing — Generate multiple audio files efficiently with memory management
• Cooperative cancellation — Thread-safe cancellation tokens for long-running generation tasks
• Extensible — Register custom TTS providers via TTSFactory.register_provider()

Installation

# Core only (brings torch, torchaudio, numpy, pydub)
pip install rho-tts

# With Qwen3-TTS provider
pip install rho-tts[qwen]

# With Chatterbox provider
pip install rho-tts[chatterbox]

# With validation (accent drift, STT, speaker similarity)
pip install rho-tts[validation]

# Everything
pip install rho-tts[all]

System Dependencies

• ffmpeg — Required by pydub for audio file joining
• CUDA — GPU recommended for reasonable generation speed (CPU works but is slow)

# Ubuntu/Debian
sudo apt install ffmpeg

# macOS
brew install ffmpeg

Hardware Requirements

Model	VRAM	Notes
Qwen3-TTS 0.6B	~8 GB	Smaller, faster
Qwen3-TTS 1.7B	~16 GB	Higher quality
Chatterbox	~6 GB	Good for single segments
Validation (Whisper)	~1 GB	Runs on CPU by default

Quick Start

from rho_tts import TTSFactory

# Create a TTS instance (requires a reference audio for voice cloning)
tts = TTSFactory.get_tts_instance(
    provider="qwen",
    reference_audio="my_voice.wav",
    reference_text="Transcript of my voice sample.",
)

# Generate a single file
result = tts.generate("Hello world!", "output.wav")

# Generate without saving to disk (in-memory only)
result = tts.generate("Hello world!")
print(result.audio, result.duration_sec)

# Generate a batch
results = tts.generate(
    texts=["First sentence.", "Second sentence."],
    output_path="batch_output",
)

Providers

Qwen3-TTS (default)

Best for batch generation with validation. Supports voice cloning via reference audio + text.

tts = TTSFactory.get_tts_instance(
    provider="qwen",
    reference_audio="voice.wav",
    reference_text="What the voice says in the audio file.",
    model_path="Qwen/Qwen3-TTS-12Hz-1.7B-Base",  # or local path
    batch_size=5,
    max_iterations=10,
    accent_drift_threshold=0.17,
    text_similarity_threshold=0.85,
)

Chatterbox

Best for single-segment regeneration with comprehensive validation loops.

tts = TTSFactory.get_tts_instance(
    provider="chatterbox",
    reference_audio="voice.wav",
    implementation="faster",  # rsxdalv optimizations
    max_iterations=50,
    accent_drift_threshold=0.17,
    text_similarity_threshold=0.75,
    speaker_similarity_threshold=0.85,
)

Configuration

All thresholds and parameters can be set via constructor kwargs:

Parameter	Default	Description
device	"cuda"	"cuda" or "cpu"
seed	789	Random seed for reproducibility
deterministic	False	Deterministic CUDA ops (slower)
phonetic_mapping	{}	Word-to-pronunciation overrides
max_iterations	10/50	Max validation retry loops
accent_drift_threshold	0.17	Max accent drift probability
text_similarity_threshold	0.85/0.75	Min STT text match score
batch_size	5	Texts per batch (Qwen only)

Custom Providers

Register your own TTS implementation:

from rho_tts import BaseTTS, TTSFactory

class MyTTS(BaseTTS):
    def _generate_audio(self, text, **kwargs):
        # Your model inference here — return a torch.Tensor
        ...

    @property
    def sample_rate(self):
        return 24000

TTSFactory.register_provider("my_tts", MyTTS)
tts = TTSFactory.get_tts_instance(provider="my_tts")

Validation Pipeline

When validation deps are installed (pip install rho-tts[validation]), generated audio goes through:

1. Accent drift detection — A trained classifier predicts the probability that the voice has drifted from the target accent. Samples exceeding the threshold are regenerated.

2. STT text matching — Whisper transcribes the audio and compares it against the intended text using fuzzy matching with number normalization.

3. Speaker similarity — Cosine similarity between the generated audio's speaker embedding and the reference voice embedding.

Training the Accent Drift Classifier

Prepare a dataset with good/ and bad/ subdirectories containing .wav files, then train a classifier. Models can be trained globally or per-voice.

Per-voice models (recommended)

Each voice can have its own classifier, stored at ~/.rho_tts/models/{voice_id}_classifier.pkl. This gives better accuracy since accent drift patterns differ between voices.

# CLI
python -m rho_tts.validation.classifier.trainer \
    --dataset-dir /path/to/dataset \
    --voice-id my_voice

# Library
from rho_tts.validation.classifier.trainer import train

train(dataset_dir="/path/to/dataset", voice_id="my_voice")

During generation, set voice_id on the TTS instance to use the per-voice model automatically:

tts = TTSFactory.get_tts_instance(provider="qwen", reference_audio="voice.wav", reference_text="...")
tts.voice_id = "my_voice"
tts.generate(texts, "output")  # uses ~/.rho_tts/models/my_voice_classifier.pkl

Global model

A global model is used as a fallback when no per-voice model exists.

# Train a global model
python -m rho_tts.validation.classifier.trainer --dataset-dir /path/to/dataset

# Or specify an explicit output path
python -m rho_tts.validation.classifier.trainer \
    --dataset-dir /path/to/dataset \
    --output /path/to/voice_quality_model.pkl

Model lookup order

When predicting accent drift, the classifier checks for models in this order:

1. Per-voice model at ~/.rho_tts/models/{voice_id}_classifier.pkl
2. Explicit path passed via model_path parameter
3. RHO_TTS_CLASSIFIER_MODEL environment variable
4. Bundled global model

# Override the global model path via environment variable
export RHO_TTS_CLASSIFIER_MODEL=/path/to/voice_quality_model.pkl

Web UI

A Gradio-based web interface for interactive TTS generation, voice management, and model configuration.

Installation

# From PyPI (once published)
pip install rho-tts[ui]

# From local source
pip install -e ".[ui]"

Launch

# CLI entry point
rho-tts-ui

# Or as a Python module
python -m rho_tts.ui

# With options
rho-tts-ui --host 0.0.0.0 --port 8080 --device cpu --share

Flag	Default	Description
--config	~/.rho_tts/config.json	Path to config JSON file
--host	127.0.0.1	Server bind address
--port	7860	Server port
--device	cuda	cuda or cpu
--share	off	Create a public Gradio link

The config path can also be set via the RHO_TTS_CONFIG environment variable.

Tabs

• Generate — Select a model and voice, enter text, and generate audio with real-time playback. Includes phonetic mapping overrides per voice/model pair.
• Voices — Upload reference audio and transcripts to create reusable voice profiles (stored in ~/.rho_tts/voices/).
• Models — Configure TTS providers with custom thresholds and parameters.

Cancellation

For long-running generation in web servers or UIs:

from rho_tts import CancellationToken, TTSFactory

token = CancellationToken()

# In worker thread
tts = TTSFactory.get_tts_instance(provider="qwen", reference_audio="voice.wav", reference_text="...")
result = tts.generate(texts, "output", cancellation_token=token)

# In controller thread (e.g., on user cancel button)
token.cancel()

License

MIT