Transcription

Scripts for transcribing — and diarizing — audio files using HF Buckets and Jobs.

Quick Start

Scripts run directly from their Hub URL — no clone or local checkout needed:

# 1. Download audio from Internet Archive straight into a bucket
hf jobs uv run \
    -v hf://buckets/user/audio-files:/output \
    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/download-ia.py \
    SUSPENSE /output

# 2. Transcribe — audio bucket in, transcript bucket out
hf jobs uv run --flavor l4x1 -s HF_TOKEN \
    -e UV_TORCH_BACKEND=cu128 \
    -v hf://buckets/user/audio-files:/input:ro \
    -v hf://buckets/user/transcripts:/output \
    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/cohere-transcribe.py \
    /input /output --language en --compile

# Or: transcribe + figure out WHO said what (speaker diarization + timestamps)
hf jobs uv run --flavor l4x1 -s HF_TOKEN \
    -e UV_TORCH_BACKEND=cu128 \
    -v hf://buckets/user/audio-files:/input:ro \
    -v hf://buckets/user/transcripts:/output \
    https://huggingface.co/datasets/uv-scripts/transcription/raw/main/moss-transcribe-diarize.py \
    /input /output --emit-txt

No download/upload step. Buckets are mounted directly as volumes via hf-mount.

Local dev: if you've cloned this repo, swap the URL for the local filename (e.g. cohere-transcribe.py /input /output ...).

Scripts

Transcription

Script	Model	Backend	Output	Speed
cohere-transcribe.py	Cohere Transcribe (2B)	transformers	.txt	161x RT (A100)
cohere-transcribe-vllm.py	Cohere Transcribe (2B)	vLLM nightly	.txt	214x RT (A100)
easytranscriber-transcribe.py	Cohere Transcribe 2B (default) or Whisper variants	easytranscriber	JSON word timestamps (+ optional .txt / .srt)	42.9x RT (L4)
moss-transcribe-diarize.py	MOSS-Transcribe-Diarize (0.9B)	transformers (remote code)	JSON speaker segments {start, end, speaker, text} (+ optional .txt / .srt)	3.2x RT (A10G, 74-min file)
moss-transcribe-diarize-server.py	MOSS-Transcribe-Diarize (0.9B)	in-job sgl-omni server	JSON speaker segments (+ optional .txt)	47.4x RT aggregate (A100, 6 streams)

cohere-transcribe.py (recommended for plain text) — uses model.transcribe() with automatic long-form chunking, overlap, and reassembly. Stable dependencies.

cohere-transcribe-vllm.py — experimental vLLM variant. Faster but requires nightly vLLM and has minor duplication at chunk boundaries.

easytranscriber-transcribe.py — when you need word-level timestamps (subtitles, search indexing, forced alignment). Runs VAD → ASR → wav2vec2 emissions → forced alignment. Defaults to the Cohere backend so you get the same model as the other scripts with alignment on top; swap to --backend ct2 + a Whisper model for languages Cohere doesn't cover (e.g. Swedish via KBLab/kb-whisper-large).

moss-transcribe-diarize.py — when you need who said what. Joint transcription + speaker diarization + timestamps in one generation pass (no separate ASR/diarization/alignment stages). 128k context handles up to ~90 min of audio per file internally, so files are never pre-chunked and the anonymous speaker labels ([S01], [S02], ...) stay consistent across the whole recording. English + Chinese; also accepts video containers (mp4, mkv, ...); supports hotword biasing for names/jargon.

moss-transcribe-diarize-server.py — same model at ~12x the throughput for many files: serves it behind sglang-omni inside the job and posts files concurrently (continuous batching). Splits long tapes into ≤55-min clips to fit the context window (speaker labels reset between clips, recorded as part on each segment) and automatically continues from the last timestamp when the model stops early, reporting coverage_s per file. Needs a100-large — 24 GB cards OOM on long-tape KV. The hf jobs run command lives in the script docstring (server + driver in one job).

Options — cohere-transcribe.py / cohere-transcribe-vllm.py

Flag	Default	Description
--language	required	en, de, fr, it, es, pt, el, nl, pl, ar, vi, zh, ja, ko
--compile	off	torch.compile encoder (one-time warmup, faster after)
--batch-size	16	Batch size for inference
--max-files	all	Limit files to process (for testing)

Options — easytranscriber-transcribe.py

Flag	Default	Description
--language	required	ISO 639-1 code. Cohere supports the same 14 languages as above; ct2/hf support any Whisper language
--backend	cohere	cohere, ct2 (CTranslate2 Whisper, fastest for Whisper), or hf (transformers)
--transcription-model	Cohere 2B / distil-whisper-large-v3.5	HF model ID; override to use KB-Whisper, Whisper-large-v3, etc.
--emissions-model	per-language default	wav2vec2 for forced alignment: en→wav2vec2-base-960h, sv→voxrex-swedish, else→facebook/mms-1b-all
--vad	silero	silero (no auth) or pyannote (requires accepting terms + HF_TOKEN)
--tokenizer-lang	derived from --language	NLTK Punkt language name for sentence tokenization
--emit-txt	off	Also write .txt transcripts alongside the JSON alignments
--emit-srt	off	Also write .srt subtitles derived from alignment segments
--batch-size-features	8	Feature-extraction batch size
--batch-size-transcribe	16	ASR batch size (where backend supports it)
--max-files	all	Limit files to process (for testing)

Options — moss-transcribe-diarize-server.py

Flag	Default	Description
--concurrency	4	Concurrent transcription requests (KV-cache bound; 6 works on A100)
--part-minutes	55	Clip length for splitting long audio (longest that fits the context window). Speaker labels reset between clips
--server	http://127.0.0.1:8000	sgl-omni server URL (in-job localhost by default)
--request-timeout	3600	Per-request timeout in seconds
--emit-txt	off	Also write .txt transcripts
--max-files	all	Limit files to process (for testing)

Options — moss-transcribe-diarize.py

Flag	Default	Description
--max-new-tokens	0 (auto)	Token budget per file. Auto scales with audio duration (min 5120, max 65536). Output JSON sets "truncated": true if the budget was hit — re-run with a higher value
--hotwords	none	Comma-separated terms (names, companies, jargon) appended to the prompt to bias recognition
--prompt	built-in	Full prompt override (replaces the built-in transcribe+diarize prompt)
--emit-txt	off	Also write .txt transcripts ([start - end] SPEAKER: text per line)
--emit-srt	off	Also write .srt subtitles with speaker prefixes
--max-files	all	Limit files to process (for testing)

Benchmarks

CBS Suspense (1940s radio drama), 66 episodes, 33 hours of audio.

cohere-transcribe.py (plain text):

GPU	Time	RTFx
A100-SXM4-80GB	12.3 min	161x realtime
L4	~64s / 30 min episode	28x realtime

easytranscriber-transcribe.py (JSON alignments + optional .txt/.srt; VAD → ASR → wav2vec2 → forced alignment):

GPU	Time	RTFx	Output
L4	46.2 min	42.9x realtime	66 JSON + SRT + TXT (42,633 segments, 295k words)

moss-transcribe-diarize.py (Apollo 11 mission audio, one 74-min multi-speaker tape):

GPU	Time	RTFx	Output
A10G	23.3 min	3.2x realtime	743 segments, 7 speakers, 23k tokens, no truncation

Long files are generation-bound (the whole diarized transcript is decoded in one pass), so RTFx drops as recordings grow; short clips run far faster. l4x1 also works — same class of GPU.

moss-transcribe-diarize-server.py (Apollo 11 mission audio from the Internet Archive — the full collection in one job):

GPU	Audio	Time	RTFx	Cost	Output
A100	174.5 h (103 tapes)	3.8 h	47.4x realtime aggregate	$9.46	45k speaker segments, 97% coverage

Result published as davanstrien/apollo-11-diarized with a searchable demo at davanstrien/apollo-11-search.

Data

Script	Description
download-ia.py	Download audio from Internet Archive into a mounted bucket

Serve as a live endpoint

MOSS-Transcribe-Diarize can be served as an OpenAI-compatible transcription API on Jobs (see Serving on Jobs). 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:

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.

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 — the tag below is the tested pin; newer nightlies should also work. The clone + fresh-venv sequence is upstream's documented install and adds under a minute:

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:

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>.

Notes

• Gated model: Accept terms at the model page before use.
• Tokenizer workaround: cohere-transcribe.py applies a one-line patch for a tokenizer compat issue. Will be removed once upstream fixes land (model discussion).
• easytranscriber: the Cohere backend requires transformers>=5.4.0 (pinned in the script). Pyannote VAD is gated — accept terms at pyannote/segmentation-3.0 and pyannote/speaker-diarization-3.1 if using --vad pyannote. Otherwise stick with the default Silero VAD.
• moss-transcribe-diarize: not gated (Apache 2.0). Uses trust_remote_code=True (model code lives in the model repo); inference helpers install from the model's GitHub repo pinned to a commit (the package isn't on PyPI). Generation time scales with audio length — long multi-speaker recordings emit tens of thousands of tokens, so watch the truncated flag in the output JSON.
• 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.