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evals-scyllasband

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Runtime at main 57271a0: release v4-20261106 (WebGPU and LiteRT bundles)

ec0b1b6 verified 8 days ago

1.89 kB

# Open TTS Leaderboard stage-1 image for Scylla's Band (spybyscript/scyllasband).
# The eval scripts are injected at job time (open-tts-leaderboard/scyllasband/run_eval.py); this image only
# holds the runtime and the model. GPU: ONNX Runtime with CUDA on the WebGPU bundle. CPU: the LiteRT bundle.
FROM nvidia/cuda:12.9.0-runtime-ubuntu24.04

ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y --no-install-recommends \
    python3 \
    python3-pip \
    git \
    libsndfile1 \
    && rm -rf /var/lib/apt/lists/*
RUN ln -sf /usr/bin/python3 /usr/bin/python
ENV PIP_BREAK_SYSTEM_PACKAGES=1

WORKDIR /app

# The runtime at a pinned commit. The `cuda` extra is onnxruntime-gpu with its CUDA and cuDNN pip libraries;
# LiteRT (ai-edge-litert) comes with the package. `datasets<4.0` decodes the eval sets without torchcodec.
ARG SCYLLASBAND_COMMIT=57271a01f793ec0ed0baa1fd47eb2383518b9e79
RUN pip install --no-cache-dir \
    "scyllasband[cuda] @ git+https://github.com/lowkeytea/scyllasband@${SCYLLASBAND_COMMIT}" \
    soundfile "datasets<4.0" tqdm

# Both bundles of the runtime's pinned release, validated on download, baked in so jobs start without fetching.
RUN python -m scyllasband download --flavor webgpu --models-dir /app/models --yes \
    && python -m scyllasband download --flavor litert --models-dir /app/models --yes

# Build-time smoke test on the CPU (the build has no GPU): load the LiteRT bundle and speak one line.
RUN python -c "from scyllasband import ScyllasBandRuntime, SynthesisRequest; \
rt = ScyllasBandRuntime.from_bundle('/app/models/litert'); \
r = rt.synthesize(SynthesisRequest(text='Build check.', voice_id='tuesday', seed=1)); \
print('ok', r.audio.size / r.sample_rate, 's')"

ENTRYPOINT ["bash"]

# Keep-alive CMD so the Space runtime stays healthy; `docker run` overrides it.
EXPOSE 7860
CMD ["-c", "python3 -m http.server 7860"]

	# Open TTS Leaderboard stage-1 image for Scylla's Band (spybyscript/scyllasband).
	# The eval scripts are injected at job time (open-tts-leaderboard/scyllasband/run_eval.py); this image only
	# holds the runtime and the model. GPU: ONNX Runtime with CUDA on the WebGPU bundle. CPU: the LiteRT bundle.
	FROM nvidia/cuda:12.9.0-runtime-ubuntu24.04

	ENV DEBIAN_FRONTEND=noninteractive
	RUN apt-get update && apt-get install -y --no-install-recommends \
	python3 \
	python3-pip \
	git \
	libsndfile1 \
	&& rm -rf /var/lib/apt/lists/*
	RUN ln -sf /usr/bin/python3 /usr/bin/python
	ENV PIP_BREAK_SYSTEM_PACKAGES=1

	WORKDIR /app

	# The runtime at a pinned commit. The `cuda` extra is onnxruntime-gpu with its CUDA and cuDNN pip libraries;
	# LiteRT (ai-edge-litert) comes with the package. `datasets<4.0` decodes the eval sets without torchcodec.
	ARG SCYLLASBAND_COMMIT=57271a01f793ec0ed0baa1fd47eb2383518b9e79
	RUN pip install --no-cache-dir \
	"scyllasband[cuda] @ git+https://github.com/lowkeytea/scyllasband@${SCYLLASBAND_COMMIT}" \
	soundfile "datasets<4.0" tqdm

	# Both bundles of the runtime's pinned release, validated on download, baked in so jobs start without fetching.
	RUN python -m scyllasband download --flavor webgpu --models-dir /app/models --yes \
	&& python -m scyllasband download --flavor litert --models-dir /app/models --yes

	# Build-time smoke test on the CPU (the build has no GPU): load the LiteRT bundle and speak one line.
	RUN python -c "from scyllasband import ScyllasBandRuntime, SynthesisRequest; \
	rt = ScyllasBandRuntime.from_bundle('/app/models/litert'); \
	r = rt.synthesize(SynthesisRequest(text='Build check.', voice_id='tuesday', seed=1)); \
	print('ok', r.audio.size / r.sample_rate, 's')"

	ENTRYPOINT ["bash"]

	# Keep-alive CMD so the Space runtime stays healthy; `docker run` overrides it.
	EXPOSE 7860
	CMD ["-c", "python3 -m http.server 7860"]