Datasets:

uv-scripts
/

transcription

# Serving transcription models on Jobs

> Part of [uv-scripts/transcription](README.md).

Two models can run as a server in a Job with an [exposed port](https://huggingface.co/docs/hub/jobs-serving), so you pay the setup once and send requests from anywhere with an HF token.

## Nemotron: serve, explore, scale

Run the server on its own to pay the ~8 min setup once, then send requests with an HF token as Bearer auth:

```bash
# Serve: the job logs https://<job_id>--8000.hf.jobs once the model is up
hf jobs uv run --detach --expose 8000 --timeout 2h \
    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py \
    --serve
# Scale: the same script as a driver, from a laptop
uv run https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py \
    ./audio ./transcripts --server https://<job_id>--8000.hf.jobs
```

For exploring, any OpenAI-compatible client works: `base_url="https://<job_id>--8000.hf.jobs/v1"`, `api_key=<HF token>`, with the audio as a `data:audio/flac;base64,...` `audio_url` part (see the model card). Measured from a laptop: a warm 2-minute clip came back in 2.4 s, and the driver did two half-hour episodes with `--timestamps` in 54 s. The first requests after start are slow (~1 min) while vLLM compiles two Triton kernels that its warmup misses. Cancel the job when done (`hf jobs cancel <job_id>`); exposed ports are billed on top of the hardware price, and each half-hour file is uploaded as ~30 MB, so for bulk work the in-job driver is still cheaper.

## MOSS-Transcribe-Diarize as an OpenAI-compatible API

MOSS-Transcribe-Diarize can be served as an OpenAI-compatible transcription API on Jobs (see [Serving on Jobs](https://huggingface.co/docs/hub/jobs-serving)). Two verified paths; both expose `/v1/audio/transcriptions` at `https://<job-id>--8000.hf.jobs` (requests need an HF token with read access to your namespace).

**vLLM** (simplest; returns the raw `[start][Sxx]text[end]` transcript). Model support was merged into vLLM on 2026-07-08 — after the v0.24.0 release — so use a commit-pinned nightly image until the next release (then plain `vllm/vllm-openai:latest` works). The image needs the audio extras installed first:

```bash
hf jobs run --detach --expose 8000 --flavor l4x1 -s HF_TOKEN --timeout 2h \
    vllm/vllm-openai:nightly-2c17d33f4291a55b447317640c81eb61077b1b00 -- \
    bash -c "pip install librosa soundfile && vllm serve OpenMOSS-Team/MOSS-Transcribe-Diarize --trust-remote-code"
```

Parse the response `text` into segments with `parse_transcript` from the model's [GitHub package](https://github.com/OpenMOSS/MOSS-Transcribe-Diarize).

**sglang-omni** (upstream's recommended production server; adds `response_format=verbose_json` with parsed speaker segments). Its prebuilt image predates this model, so install it at job start over a current [sglang nightly image](https://hub.docker.com/r/lmsysorg/sglang/tags) — the tag below is the tested pin; newer nightlies should also work. The clone + fresh-venv sequence is [upstream's documented install](https://github.com/sgl-project/sglang-omni/blob/main/docs/get_started/installation.md) and adds under a minute:

```bash
hf jobs run --detach --expose 8000 --flavor l4x1 -s HF_TOKEN --timeout 2h \
    lmsysorg/sglang:nightly-dev-cu13-20260709-074bb928 -- \
    bash -c "pip install -q uv 2>/dev/null; git clone --depth 1 https://github.com/sgl-project/sglang-omni.git && cd sglang-omni && uv venv .venv -p 3.12 && . .venv/bin/activate && uv pip install . && sgl-omni serve --model-path OpenMOSS-Team/MOSS-Transcribe-Diarize --host 0.0.0.0 --port 8000 --max-running-requests 16 --mem-fraction-static 0.80"
```

Query either server the same way (`response_format=verbose_json` on sglang-omni only; raise `max_new_tokens`, e.g. `65536`, for long recordings). **Caution — long audio on the serve path**: sglang-omni silently truncates input past ~62 minutes (observed deterministically at ~62.5 min on the same tape that transcribes fully via the transformers recipe, regardless of `max_new_tokens`). Split longer recordings before posting — `moss-transcribe-diarize-server.py` does this automatically:

```bash
curl -X POST https://<job-id>--8000.hf.jobs/v1/audio/transcriptions \
    -H "Authorization: Bearer $HF_TOKEN" \
    -F model=OpenMOSS-Team/MOSS-Transcribe-Diarize \
    -F file=@meeting.wav \
    -F response_format=verbose_json \
    -F max_new_tokens=65536
```

The job — and its billing — stops at `--timeout` or `hf jobs cancel <job_id>`.

## sglang-omni install

the `lmsysorg/sglang-omni:dev` image predates this model — don't use it. Install from git over a current `lmsysorg/sglang` nightly image as shown above, and follow the clone + fresh-venv sequence exactly: installing outside the repo skips its `[tool.uv]` dependency overrides and fails on a protobuf conflict. Once they publish a fresh image, `sgl-omni serve` alone will do.

	# Serving transcription models on Jobs

	> Part of [uv-scripts/transcription](README.md).

	Two models can run as a server in a Job with an [exposed port](https://huggingface.co/docs/hub/jobs-serving), so you pay the setup once and send requests from anywhere with an HF token.

	## Nemotron: serve, explore, scale

	Run the server on its own to pay the ~8 min setup once, then send requests with an HF token as Bearer auth:

	```bash
	# Serve: the job logs https://<job_id>--8000.hf.jobs once the model is up
	hf jobs uv run --detach --expose 8000 --timeout 2h \
	https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py \
	--serve

	# Scale: the same script as a driver, from a laptop
	uv run https://huggingface.co/datasets/uv-scripts/transcription/raw/main/nemotron-transcribe.py \
	./audio ./transcripts --server https://<job_id>--8000.hf.jobs
	```

	For exploring, any OpenAI-compatible client works: `base_url="https://<job_id>--8000.hf.jobs/v1"`, `api_key=<HF token>`, with the audio as a `data:audio/flac;base64,...` `audio_url` part (see the model card). Measured from a laptop: a warm 2-minute clip came back in 2.4 s, and the driver did two half-hour episodes with `--timestamps` in 54 s. The first requests after start are slow (~1 min) while vLLM compiles two Triton kernels that its warmup misses. Cancel the job when done (`hf jobs cancel <job_id>`); exposed ports are billed on top of the hardware price, and each half-hour file is uploaded as ~30 MB, so for bulk work the in-job driver is still cheaper.

	## MOSS-Transcribe-Diarize as an OpenAI-compatible API

	MOSS-Transcribe-Diarize can be served as an OpenAI-compatible transcription API on Jobs (see [Serving on Jobs](https://huggingface.co/docs/hub/jobs-serving)). Two verified paths; both expose `/v1/audio/transcriptions` at `https://<job-id>--8000.hf.jobs` (requests need an HF token with read access to your namespace).

	vLLM (simplest; returns the raw `[start][Sxx]text[end]` transcript). Model support was merged into vLLM on 2026-07-08 — after the v0.24.0 release — so use a commit-pinned nightly image until the next release (then plain `vllm/vllm-openai:latest` works). The image needs the audio extras installed first:

	```bash
	hf jobs run --detach --expose 8000 --flavor l4x1 -s HF_TOKEN --timeout 2h \
	vllm/vllm-openai:nightly-2c17d33f4291a55b447317640c81eb61077b1b00 -- \
	bash -c "pip install librosa soundfile && vllm serve OpenMOSS-Team/MOSS-Transcribe-Diarize --trust-remote-code"
	```

	Parse the response `text` into segments with `parse_transcript` from the model's [GitHub package](https://github.com/OpenMOSS/MOSS-Transcribe-Diarize).

	sglang-omni (upstream's recommended production server; adds `response_format=verbose_json` with parsed speaker segments). Its prebuilt image predates this model, so install it at job start over a current [sglang nightly image](https://hub.docker.com/r/lmsysorg/sglang/tags) — the tag below is the tested pin; newer nightlies should also work. The clone + fresh-venv sequence is [upstream's documented install](https://github.com/sgl-project/sglang-omni/blob/main/docs/get_started/installation.md) and adds under a minute:

	```bash
	hf jobs run --detach --expose 8000 --flavor l4x1 -s HF_TOKEN --timeout 2h \
	lmsysorg/sglang:nightly-dev-cu13-20260709-074bb928 -- \
	bash -c "pip install -q uv 2>/dev/null; git clone --depth 1 https://github.com/sgl-project/sglang-omni.git && cd sglang-omni && uv venv .venv -p 3.12 && . .venv/bin/activate && uv pip install . && sgl-omni serve --model-path OpenMOSS-Team/MOSS-Transcribe-Diarize --host 0.0.0.0 --port 8000 --max-running-requests 16 --mem-fraction-static 0.80"
	```

	Query either server the same way (`response_format=verbose_json` on sglang-omni only; raise `max_new_tokens`, e.g. `65536`, for long recordings). Caution — long audio on the serve path: sglang-omni silently truncates input past ~62 minutes (observed deterministically at ~62.5 min on the same tape that transcribes fully via the transformers recipe, regardless of `max_new_tokens`). Split longer recordings before posting — `moss-transcribe-diarize-server.py` does this automatically:

	```bash
	curl -X POST https://<job-id>--8000.hf.jobs/v1/audio/transcriptions \
	-H "Authorization: Bearer $HF_TOKEN" \
	-F model=OpenMOSS-Team/MOSS-Transcribe-Diarize \
	-F file=@meeting.wav \
	-F response_format=verbose_json \
	-F max_new_tokens=65536
	```

	The job — and its billing — stops at `--timeout` or `hf jobs cancel <job_id>`.

	## sglang-omni install

	the `lmsysorg/sglang-omni:dev` image predates this model — don't use it. Install from git over a current `lmsysorg/sglang` nightly image as shown above, and follow the clone + fresh-venv sequence exactly: installing outside the repo skips its `[tool.uv]` dependency overrides and fails on a protobuf conflict. Once they publish a fresh image, `sgl-omni serve` alone will do.