Inference Space for the Gepard autoregressive speech model
(nineninesix/gepard-1.0 | nineninesix/gepard-1.1 β a Qwen3.5 backbone with 32 FSQ audio
heads, a Q-Former voice-cloning compressor, and DPO | GRPO post-training).
speakers/*.pt), no codec pass needed at request time.cfg_scale/cfg_frames sharpen text
and voice adherence (defaults: temperature=0.3, cfg_scale=3).The Space runs on gradio.Server β a FastAPI app with Gradio's queueing
engine on top. The custom dark-themed UI in index.html is served at /
via @app.get("/"); the synthesis pipeline is exposed at
@app.api("/synthesize"), so requests flow through Gradio's queue
(concurrency control, ZeroGPU allocation, gradio_client compatibility)
while the frontend stays a self-contained HTML/CSS/JS bundle.
config.yaml selects the checkpoint, codec, preset speakers and default
generation parameters β re-point the Space without touching code.
HF_TOKEN Space secret.create_env.py orchestrates a 4-step install at app startup β it must
run before any ML import in app.py:
huggingface-hub>=1.2,<2.0 (compatible with both gradio 6.20
and nemo-toolkit).pip install --no-deps nemo-toolkit[tts]==2.4.0 (avoiding a downgrade
of hub by the resolver).transformers==5.3.0 (Qwen3.5 backbone that the
Gepard checkpoint was trained on).numpy<2.0 so the codec/NeMo stack stays on numpy 1.x.nemo-toolkit is installed at runtime (not in requirements.txt) to
keep gradio 6.20's huggingface-hub>=1.2 constraint resolvable at
build time β NeMo's transformers<=4.52 would otherwise pull hub<1.0.18 commits
13 commits
6 commits
3 commits
Inference Space for the Gepard autoregressive speech model
(nineninesix/gepard-1.0 | nineninesix/gepard-1.1 β a Qwen3.5 backbone with 32 FSQ audio
heads, a Q-Former voice-cloning compressor, and DPO | GRPO post-training).
speakers/*.pt), no codec pass needed at request time.cfg_scale/cfg_frames sharpen text
and voice adherence (defaults: temperature=0.3, cfg_scale=3).The Space runs on gradio.Server β a FastAPI app with Gradio's queueing
engine on top. The custom dark-themed UI in index.html is served at /
via @app.get("/"); the synthesis pipeline is exposed at
@app.api("/synthesize"), so requests flow through Gradio's queue
(concurrency control, ZeroGPU allocation, gradio_client compatibility)
while the frontend stays a self-contained HTML/CSS/JS bundle.
config.yaml selects the checkpoint, codec, preset speakers and default
generation parameters β re-point the Space without touching code.
HF_TOKEN Space secret.create_env.py orchestrates a 4-step install at app startup β it must
run before any ML import in app.py:
huggingface-hub>=1.2,<2.0 (compatible with both gradio 6.20
and nemo-toolkit).pip install --no-deps nemo-toolkit[tts]==2.4.0 (avoiding a downgrade
of hub by the resolver).transformers==5.3.0 (Qwen3.5 backbone that the
Gepard checkpoint was trained on).numpy<2.0 so the codec/NeMo stack stays on numpy 1.x.nemo-toolkit is installed at runtime (not in requirements.txt) to
keep gradio 6.20's huggingface-hub>=1.2 constraint resolvable at
build time β NeMo's transformers<=4.52 would otherwise pull hub<1.0.18 commits
13 commits
6 commits
3 commits