hangrylabs/faster-qwen3-tts-studio repository overview

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🎧 Listen to voice examples⁠

⁠Hangry Labs Faster Qwen3-TTS Studio

Local voice design, reference cloning and expressive speech generation in one Docker container, with a browser Studio, streaming audio, OpenAI-compatible APIs and optional MCP tools.

Publication status: v1.0, v1.0_tiny and v1.0_sources are published on Docker Hub and GHCR. The commands below pin the verified matching OCI indexes.

⁠Quick Start

With Docker, NVIDIA GPU support and a CUDA 12.8-compatible driver installed, run:

docker run -d --name faster-qwen3-tts-studio --restart unless-stopped -p 127.0.0.1:7860:7860 --gpus all -e CUDA_VISIBLE_DEVICES=0 -v faster_qwen3_tts_studio_data:/app/persistent hangrylabs/faster-qwen3-tts-studio:v1.0@sha256:a8a3643b077e05c2ccf33e9b97978f082d2e89ac33b3d1368ab0e25df628899d

Open http://localhost:7860⁠. API documentation is at http://localhost:7860/docs⁠ and the schema is at http://localhost:7860/openapi.json⁠.

The full image includes all five pinned checkpoints and can run offline after the image pull. The named volume preserves models, saved voices and scripts.

For a smaller download, use the tiny image instead:

docker run -d --name faster-qwen3-tts-studio --restart unless-stopped -p 127.0.0.1:7860:7860 --gpus all -e CUDA_VISIBLE_DEVICES=0 -v faster_qwen3_tts_studio_data:/app/persistent hangrylabs/faster-qwen3-tts-studio:v1.0_tiny@sha256:29f06b3226da4a55244727e111e7700786e53049a54560efeef4bb12a62bd0bd

Tiny downloads its models into the same volume on first use. Keep it online until the selected models are cached. Choose either full or tiny.

⁠Browser UI

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⁠Image Variants And Tags

Both variants are built to hangrylabs/faster-qwen3-tts-studio and mirrored to ghcr.io/hangry-labs/faster-qwen3-tts-studio. Full merges baked models into the same persistent cache and defaults to offline use. Tiny needs network access to populate its cache. Neither runs local ASR.

The Quick Start uses the published v1.0 release with verified top-level OCI index digests. GHCR uses the same tags and digests: replace hangrylabs/faster-qwen3-tts-studio with ghcr.io/hangry-labs/faster-qwen3-tts-studio in either command.

main publishes the moving latest and latest_tiny tags. They remain unpinned for users deliberately following development; see the version history⁠ for those commands and fixed release references.

Matching source image: hangrylabs/faster-qwen3-tts-studio:v1.0_sources@sha256:515ff1591aa438af475fb22deebb7b8efd619b6258d5f08ff5f12fb3b82b9831. Use the corresponding-source guide⁠ to extract the archives and rebuild recipes without running an inference server.

⁠What You Get

• Create voices from descriptions with 1.7B VoiceDesign, then save them as cloning references
• Clone authorized recordings with 0.6B or 1.7B Base using reference audio and its transcript
• Choose nine built-in CustomVoice speakers, with style instructions on the 1.7B model
• Generate speech in ten languages, with automatic language selection
• Use incremental playback and live text-in/audio-out cloning with 1.7B Base
• Generate multi-character dialogue with Magic, a bounded SSML subset or SSML-H
• Organize saved voices with portraits, metadata, creation recipes and voice refinement
• Keep scripts and reusable takes, with long-text planning and FIFO queue progress
• Trim audio, compare original and processed previews, and apply optional effects and mastering
• Connect OpenAI-compatible speech/voice APIs or separately enabled compact and advanced MCP tools
• Choose per-workflow defaults and GPU residency, with timing and GPU telemetry in System
• Use the browser UI in English, Norwegian Bokmål, Polish, Japanese, Simplified Chinese or Spanish

The included offline gallery has ten designed voices, multilingual speech and cloning comparisons, and downloadable voice recipes. Open Examples in the Studio or use the listening link at the top of this page.

Speech languages: Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish and Italian. Interface language is independent of speech.

Base delivery direction remains experimental; transcript-assisted cloning is recommended. The maintained Studio supports NVIDIA/Torch, with Python 3.13, Torch 2.10.0 CUDA 12.8 wheels and locally maintained CUDA graph acceleration. See source provenance⁠ for the three upstreams and incorporated revisions.

⁠Persistent Data And Runtime Settings

Reuse faster_qwen3_tts_studio_data at /app/persistent when replacing the container. It holds downloaded checkpoints, compiler caches, operator settings and explicitly saved reference voices. Deleting it removes those contents. Uploads remain temporary unless explicitly saved; prompts and generated audio are not automatically retained across browser reloads.

ASR settings saved in System > Reference transcription override the initial environment value. No local ASR model is included or started. You can enter reference transcripts manually and generate speech without an ASR connection.

Memory needs vary by model and request. System can unload all weights while keeping the UI available. Prepared clone prompts use a bounded CPU cache cleared on unload. Inference is serialized with FIFO waiting-request feedback.

The example binds HTTP to this computer only. There is no account system. QWEN_STUDIO_API_KEY optionally protects /v1 with a Bearer key, but the native and browser routes remain unauthenticated. Use trusted network controls if exposing the port elsewhere. Recording requires localhost or HTTPS and browser permission. Browser writes are restricted to the service host; direct API clients remain supported without an Origin header.

⁠API Example

The Studio serves OpenAI-compatible speech, native generation, SSML/SSML-H, Magic takes and saved voices/scripts on the same port. Opt-in compact and advanced MCP endpoints are available at /mcp/ and /mcp/advanced/; enable them in System. For remote clients, set QWEN_STUDIO_MCP_BASE_URL to the reachable service origin.

curl http://localhost:7860/v1/audio/speech \
  -H 'Content-Type: application/json' \
  -d '{"model":"qwen3-tts","input":"Good morning from Hangry Labs.","voice":"Ryan","response_format":"mp3"}' \
  --output speech.mp3

GET /v1/models and /v1/audio/voices expose the actual inventory. Saved voice ids work with the speech endpoint, including optional delivery instructions. Formats include MP3, WAV, FLAC, Opus, AAC, Ogg and raw 24 kHz mono PCM16. OpenAI input remains plain text. Native /tts/generate, /tts/stream and /tts/events support explicit input_type="ssml" or "ssml-h". See the API guide⁠ and exact SSML-H profile⁠.

For LLM integrations, the live speech API⁠ accepts arriving text and returns PCM on /tts/live. It uses 1.7B Base and a saved voice, including its reference transcript for full-reference cloning. Speaker-only conditioning is also available. The service, UI and all APIs share port 7860.

Use this Python example with httpx installed:

import base64
import json
from pathlib import Path

import httpx

options = {
    "mode": "custom",
    "model": "Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice",
    "text": "Good morning from Hangry Labs.",
    "speaker": "Ryan",
    "language": "English",
}
response = httpx.post(
    "http://localhost:7860/studio/generate",
    data={"options": json.dumps(options)},
    timeout=300,
)
response.raise_for_status()
Path("speech.wav").write_bytes(base64.b64decode(response.json()["audio_b64"]))

GET /studio/capabilities lists models, speakers, languages and limits. GET /studio/status reports readiness, active model and queue state. POST /studio/generate/stream returns server-sent events containing progress and base64 WAV chunks. It is not a raw audio stream. Cloning accepts an uploaded reference or saved voice_id alongside the multipart options JSON field.

⁠Responsible Use And Privacy

Only clone voices with the necessary consent and recording rights. Do not use synthetic speech for deceptive impersonation, fraud or harassment. Review output before publication, including commercial use.

Generation runs locally. First-use checkpoint downloads contact the model host. Reference transcription sends audio to the operator-selected service only when explicitly requested. Saved reference recordings remain in the product volume.

⁠Project Links

• Project website and documentation⁠
• GitHub repository and README⁠
• Studio guide⁠
• Development tasks⁠
• Container builds and releases⁠
• Original accelerated engine⁠
• Qwen3-TTS models and research⁠
• Hangry Labs⁠

⁠Attribution And Licensing

The accelerated engine comes from Faster Qwen3-TTS by Andres Marafioti; Qwen provides the model weights and core research. This independently maintained Hangry Labs fork adds the Docker workflow, Studio and product integrations. Streaming adaptations and the VoxCPMTTS-based UI retain their source notices.

The Python distribution is MIT AND Apache-2.0. Models and runtime dependencies retain separate terms; a root permissive license does not replace those terms. Images retain runtime component notices under /app/licenses/runtime. Redistribution must also meet the applicable native-component and corresponding-source requirements described in the licensing guide.

See NOTICE⁠, Third-Party Notices⁠ and the licensing guide⁠. Report product issues in the GitHub issue tracker⁠.