moss-transcribe-diarize

Speech-to-text

MOSS-Transcribe-Diarize transcribes speech, identifies speakers, and adds timestamps in a single pass. It handles multilingual audio and serves the OpenAI-compatible transcription endpoint, so a client that already calls /v1/audio/transcriptions gets diarized output without changing its request shape.

Setup

Sign in to Baseten

uvx truss login --browser

Install the OpenAI SDK

uv pip install openai

This preset serves MOSS-Transcribe-Diarize on one H100, with SGLang Omni pinned to v0.1.2, optimized for single-pass transcription that returns speaker labels and timestamps alongside the transcript.

Hardware

H100

Engine

SGLang (46235435997d1fa9… build)

Concurrency

128

Write the config

Create and move into the project directory:

mkdir moss-transcribe-diarize-latency && cd moss-transcribe-diarize-latency

Then create a file named config.yaml and paste the following:

config.yaml

model_name: moss-transcribe-diarize
model_metadata:
  repo_id: OpenMOSS-Team/MOSS-Transcribe-Diarize
  # No example_model_input on purpose: /v1/audio/transcriptions only accepts
  # multipart/form-data (file upload), and the CI predict smoke POSTs
  # example_model_input as JSON, which the server rejects with 422.
  tags:
    - openai-compatible
    - audio
    - asr
    - diarization
secrets:
  hf_access_token: null
weights:
  - source: "hf://OpenMOSS-Team/MOSS-Transcribe-Diarize@e8681d68e7042738ffca8ac8212bc8fcb1131ab8"
    mount_location: "/app/checkpoint/model"
    auth_secret_name: "hf_access_token"
environment_variables:
  HF_HOME: /app/models/huggingface
  TORCHINDUCTOR_CACHE_DIR: /app/models/inductor
  TRITON_CACHE_DIR: /app/models/triton
base_image:
  # Runtime prerequisites image recommended by the v0.1.2 installation guide,
  # pinned by digest because upstream publishes it under the mutable `dev` tag.
  image: "lmsysorg/sglang-omni:dev@sha256:46235435997d1fa93fc81fb1c2d5b7fd8470d77395a5c348c0176094ffddf95e"
build_commands:
  # Install the exact v0.1.2 release package; its flash-attn-4 dependency is a beta.
  - uv venv /opt/sglang-omni-v0.1.2 --python 3.12
  # Pin the resolved flash-attn-4 beta so rebuilds stay reproducible; its 4.x line
  # publishes only pre-releases, so --prerelease=allow is still required to install it.
  - uv pip install --prerelease=allow --python /opt/sglang-omni-v0.1.2/bin/python "sglang-omni==0.1.2" "flash-attn-4==4.0.0b19"
docker_server:
  start_command: >-
    /opt/sglang-omni-v0.1.2/bin/sgl-omni serve
    --model-path /app/checkpoint/model
    --port 8000
    --max-running-requests 16
    --cuda-graph-max-bs 16
    --mem-fraction-static 0.80
  readiness_endpoint: /health
  liveness_endpoint: /health
  predict_endpoint: /v1/audio/transcriptions
  server_port: 8000
resources:
  accelerator: H100
  cpu: "8"
  memory: 32Gi
  use_gpu: true
runtime:
  predict_concurrency: 128

The deployment exposes /v1/audio/transcriptions, the same OpenAI-compatible route a plain Whisper deployment serves, so existing clients keep their request shape. Pass response_format=verbose_json to get parsed speaker segments instead of a flat transcript, and raise max_new_tokens for long recordings. Weights are pinned to a Hugging Face revision and the runtime image to a digest, so a rebuild reproduces the same engine.

Flags

The start_command passes these flags to the engine. Each one controls a runtime or serving behavior:

Flag	Value	What it does
`--max-running-requests`	`16`	Maximum requests the SGLang scheduler runs concurrently. Arrivals past the cap queue instead of running.
`--cuda-graph-max-bs`	`16`	Largest batch size SGLang captures CUDA graphs for. Larger values capture more graphs and use more memory.
`--mem-fraction-static`	`0.80`	Fraction of GPU memory SGLang reserves for model weights and the KV cache pool. Lower it to leave room for activations and CUDA graph buffers, at the cost of peak concurrency.

Deploy

Push the config to Baseten:

uvx truss push

You should see output similar to:

✨ Model moss-transcribe-diarize-latency was successfully pushed ✨

   Model ID:      abc1d2ef
   Deployment ID: xyz123
   Endpoint:      model-abc1d2ef.api.baseten.co
   Logs:          https://app.baseten.co/models/abc1d2ef/logs/xyz123

truss push prints your model ID (abc1d2ef in the example). The examples below use it wherever you see {model_id}, and read your API key from the BASETEN_API_KEY environment variable.

Call the model

Your deployment serves an OpenAI-compatible chat completions API at /v1/audio/transcriptions that accepts audio inputs. Send audio as an audio_url content item on a chat message. The model returns the transcription as the assistant message content.

Python
cURL

main.py

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["BASETEN_API_KEY"],
    base_url="https://model-{model_id}.api.baseten.co/environments/production/sync/v1",
)

response = client.chat.completions.create(
    model="OpenMOSS-Team/MOSS-Transcribe-Diarize",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "audio_url",
                    "audio_url": {
                        "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav"
                    },
                }
            ],
        }
    ],
)

print(response.choices[0].message.content)

curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "OpenMOSS-Team/MOSS-Transcribe-Diarize",
    "messages": [
      {"role": "user", "content": [
        {"type": "audio_url", "audio_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav"}}
      ]}
    ]
  }'

Examples

Models

Engines

Custom Docker servers

Custom Python models

Chains

moss-transcribe-diarize

Setup

Hardware

Engine

Concurrency

Write the config

Flags

Deploy

Call the model

Next steps

Call your model

Autoscaling

​Setup

Hardware

Engine

Concurrency

​Write the config

​Flags

​Deploy

​Call the model

​Next steps

Call your model

Autoscaling

Setup

Write the config

Flags

Deploy

Call the model

Next steps