vocal-helper 0.4.2

Vocal Helper — async producer/consumer pipeline turning a live PCM stream into diarized, transcribed utterances and (optionally) a rolling LLM summary. Stages: Voice Activity Detection → online speaker diarization (Hungarian-style cosine clustering on backend embeddings) → STT (pywhispercpp turbo) → optional analyst (Gemma 3 4b via Ollama).

Vocal Helper

🇫🇷 · 🇬🇧

Vocal Helper belongs to a collection of libraries called AI Helpers developed for building Artificial Intelligence.

🌍 AI Helpers

Vocal Helper is an async producer/consumer pipeline turning audio into diarized, transcribed utterances — and (optionally) a rolling LLM summary of the conversation. Two paths ship :

• Online (voh.Pipeline) — live PCM stream → live transcript + live summary. Each stage runs at its own cadence, decoupled by bounded queues. The STT stage warms up on start so the first caption doesn't stall on whisper's cold inference.
• Offline (voh.OfflinePipeline) — full audio buffer → highest-quality diarization (pyannote 3.1 runs the whole meeting in one call — the 2026-07-14 offline map-reduce study found whole-buffer strictly best for DER; chunk-and-stitch survives only as a memory backstop past ~1 h) → full-throttle batched transcript (consecutive segments concatenated into ≤ 24 s whisper calls — ~6.5× lower RTF at better WER per the 2026-07-09 sweep) → summary. Opt back into per-segment ASR with OfflinePipelineConfig(asr={"batch": False}).

Pipelines

Every edge is a bounded asyncio.Queue ; every stage is its own coroutine. Colours follow the AI Helpers palette.

Online (streaming)

flowchart LR
    S([Source<br/><i>PCM frames</i>]):::source
      --> V[VAD<br/><i>Silero v5 ONNX</i>]:::vad
      --> D[Online Diar<br/><i>TitaNet · cosine clustering</i>]:::diar
      --> A[STT<br/><i>whisper.cpp turbo</i>]:::asr
      -.-> L[LLM analyst<br/><i>gemma3:4b · rolling summary</i>]:::llm

    classDef source fill:#CCE4FF,stroke:#007AFF,stroke-width:2px,color:#0b3d91
    classDef vad    fill:#00ffef,stroke:#79dbdc,stroke-width:2px,color:#003b3c
    classDef diar   fill:#EFDCF8,stroke:#AF52DE,stroke-width:2px,color:#4a1063
    classDef asr    fill:#FFEACC,stroke:#FF9500,stroke-width:2px,color:#5a3300
    classDef llm    fill:#D4F5D9,stroke:#28CD41,stroke-width:2px,color:#144d1e,stroke-dasharray: 5 5

The dashed edge marks the analyst as optional (llm=None disables it).

Offline (batch)

flowchart LR
    S([Source<br/><i>full PCM buffer</i>]):::source
      --> D[Offline Diar<br/><i>pyannote 3.1<br/>whole-buffer</i>]:::diar
      --> A[STT<br/><i>whisper.cpp turbo</i>]:::asr
      -.-> L[LLM analyst<br/><i>gemma3:4b · rolling summary</i>]:::llm

    classDef source fill:#CCE4FF,stroke:#007AFF,stroke-width:2px,color:#0b3d91
    classDef diar   fill:#EFDCF8,stroke:#AF52DE,stroke-width:2px,color:#4a1063
    classDef asr    fill:#FFEACC,stroke:#FF9500,stroke-width:2px,color:#5a3300
    classDef llm    fill:#D4F5D9,stroke:#28CD41,stroke-width:2px,color:#144d1e,stroke-dasharray: 5 5

No VAD in the offline path — the diarizer consumes the whole buffer and does its own segmentation.

Stage	Backend	Notes
VAD (online only)	Silero v5 ONNX (CPU)	32 ms window, activity_threshold=0.5, default min_silence_ms=300.
Online diarization	nvidia/titanet_large (NeMo, default) or pyannote/embedding	Per-segment embedding + cosine-distance running-mean clustering, join_threshold=0.30. Default backend switched to NeMo by the 2026-06-30 embedding sweep (studies/diar_embedding_backend.py): TitaNet has +76 % separability margin (inter − intra median cosine = 0.354 vs pyannote 0.201) on AMI dev-slice, at 7× per-call latency (45 ms vs 6 ms — still negligible per voiced segment). Pass backend='pyannote' to skip the ~ 5 GB NeMo install.
Offline diarization	pyannote/speaker-diarization-3.1 (default) or nvidia/diar_sortformer_v1 (NeMo)	Whole-buffer call. Inputs longer than ideal_duration_s (3600 s for pyannote — effectively whole-buffer, chunking is a memory backstop only; 60 s for NeMo, forced by its Sortformer 90 s cap) are auto-chunked with 10 s overlap and stitched by cosine AHC at stitch_threshold=0.35. The 2026-07-14 offline map-reduce study found whole-buffer strictly best for DER (0.143 vs 0.170 at 300 s).
STT	pywhispercpp turbo	large-v3-turbo-q5_0 by default. Word timestamps on. Runs in a thread pool so the event loop never stalls. Strongly recommended: supply initial_prompt (domain bias) — cuts WER 15-25 pp and saves up to 39 % RTF per the 2026-06-30 sweep (studies/whisper_prompt_lang_lock.py).
LLM analyst (optional)	Ollama-served Gemma 3 4b (gemma3:4b)	Rolling summary of everything older than 60 s. The recent 60 s window is kept verbatim. Summary refreshes every 60 s of evicted content (flush_every_s=60). Default model gemma3:4b selected by the 2026-06-30 7-model Pareto sweep (studies/llm_model_size_sweep.py): it dominates gemma4:e4b-mlx on BOTH RTF (0.099 vs 0.313, 3× faster) AND cos_sim (0.466 vs 0.420). Pareto front also exposes gemma4:12b-mlx (RTF 2.45, cos_sim 0.496) for offline-batch quality runs, and qwen2.5:3b (RTF 0.043) for tight RTF budgets.

Quickstart

More recipes? See EXAMPLES.md for a self-contained, copy-runnable cookbook of the common workflows (live mic, URL replay, offline batch, subscribers, multi-surface exposure).

Deploying on a GPU server? See TECHNICAL_STACK.md for the full stack recipe : CUDA + PyTorch, whisper.cpp with GGML_CUDA=on, pyannote 3.1 on MPS/CUDA, Ollama systemd, Docker Compose template, expected RTFs per GPU, and a 10-step reproducible install manifest covering the whole AI Helpers suite (os-helper, audio-helper, podcast-helper, youtube-helper, vocal-helper, music-helper).

Install

Prerequisites — Python 3.10–3.13 and git, ffmpeg, PortAudio, cross-platform:

• 🍎 macOS (Homebrew): brew install python git ffmpeg portaudio
• 🐧 Ubuntu/Debian: sudo apt update && sudo apt install -y python3 python3-pip git ffmpeg portaudio19-dev
• 🪟 Windows (PowerShell): winget install Python.Python.3.12 Git.Git Gyan.FFmpeg (PortAudio ships inside the Python wheels)

Then install the package:

pip install 'vocal-helper[all]'

The [all] extra brings the mic source, both diarization backends (NeMo — the default — and pyannote), and Ollama. Pick à la carte if you don't need everything :

Extra	Brings	Required when
(none)	pywhispercpp, silero-vad, audio-helper	File / numpy sources, no diarization
[mic]	capture-helper	Live microphone source
[pyannote]	pyannote.audio	diar={'backend': 'pyannote'} (lighter ~500 MB fallback)
[nemo]	torch, nemo-toolkit[asr]	diar={'backend': 'nemo'} (default — TitaNet, ~5 GB)
[llm]	ollama	llm={'model': 'gemma3:4b'} (default)
[all]	All of the above	One-line install

You also need Ollama running locally if you enable the LLM analyst :

ollama pull gemma3:4b   # default (or gemma4:12b-mlx for max quality, qwen2.5:3b for min RTF)
ollama serve   # usually launched at install time

Model weights — no HuggingFace needed

All model weights ship in a single self-hosted diarization-engines bundle (offline pyannote 3.1, NeMo Sortformer, the online pyannote/embedding embedder, SpeechBrain VoxLingua107). Point vocal-helper at it once and the whole stack runs HuggingFace-free — no token, no gated downloads, HF_HUB_OFFLINE=1 safe.

Configure it in settings.yaml (the only config the project needs):

cp settings.yaml.example settings.yaml
# settings.yaml already contains:
#   engines:
#     diarization_url: https://deraison.ai/diarization-engines.zip
# settings.yaml is git-ignored.

What the URL is

https://deraison.ai/diarization-engines.zip is a self-hosted ZIP (~800 MB) that mirrors every gated/hub-hosted model the pipeline needs, so the project never has to authenticate against HuggingFace. It contains:

Folder	Weights	Used by
pyannote-3.1/	segmentation-3.0 + wespeaker .bin + a local config.yaml	offline diarization
nemo-sortformer/	diar_sortformer_4spk-v1.nemo	offline diarization (NeMo)
pyannote-embedding/	embedding .bin	online diarization
speechbrain-voxlingua107/	ECAPA VoxLingua107 snapshot	language-ID cross-check
manifest.json	sha256 + sizes	integrity check on download

On first use it is downloaded once, verified against manifest.json, and cached under ~/.cache/vocal-helper; later runs load straight from the cache. Set $VH_DIARIZATION_ENGINES to a local directory (or your own mirror URL) for air-gapped / self-hosted deploys. TitaNet (the default online-diar embedder) loads from NVIDIA NGC, also without HuggingFace.

Live microphone → terminal

# No token, no HuggingFace — weights come from the diarization-engines bundle.
vocal-helper mic --llm

Python API

import asyncio
import vocal_helper as voh

async def main():
    pipeline = voh.Pipeline(
        source=lambda: voh.sources.from_microphone(),
        config=voh.PipelineConfig(
            diar={"backend": "pyannote"},
            asr={"model": "large-v3-turbo-q5_0", "language": "fr"},
            llm={"model": "gemma3:4b"},   # remove to disable
        ),
    )
    async for ev in pipeline.run():
        if "text" in ev:
            print(f"[{ev['t0']:.1f} {ev['speaker']}] {ev['text']}")
        elif "summary" in ev:
            print(f"--- rolling summary ---\n{ev['summary']}")

asyncio.run(main())

Replay a WAV through the online pipeline

vocal-helper file path/to/conversation.wav --llm

The file source preserves real-time pacing by default ; pass --no-real-time for as-fast-as-possible batch processing.

Offline batch on a WAV (full-buffer pyannote 3.1)

import asyncio, vocal_helper as voh

async def main():
    pipeline = voh.OfflinePipeline(
        source=lambda: voh.sources.from_wav_file(
            "meeting.wav", real_time=False
        ),
        config=voh.OfflinePipelineConfig(
            diar={"backend": "pyannote"},   # or "nemo" for ≤ 60 s clips
            asr={"language": "en"},
            llm={"model": "gemma3:4b"},    # remove to disable
        ),
    )
    async for ev in pipeline.run():
        if "text" in ev:
            print(f"[{ev['t0']:.1f} {ev['speaker']}] {ev['text']}")
        elif "summary" in ev:
            print(f"--- digest ---\n{ev['summary']}")

asyncio.run(main())

When to use which :

Use-case	Pipeline	Why
Live mic / live stream	Pipeline	Real-time diarization + transcript at RTF ≈ 0.03 (NeMo) or RTF ≈ 0.06 (pyannote).
Meeting recording / podcast / lecture / voicemail batch	OfflinePipeline	Pyannote 3.1 on the full audio is the highest-DER answer per the pdbms 2026-06-29 canonical study — median DER 0.116 on AMI dev-slice, inside Bredin 2023's band.
≤ 60 s clips, fast turn-around	OfflinePipeline(backend='nemo')	NeMo Sortformer end-to-end attribution gives conf ≈ 0 ; RTF ≈ 0.004.

Multi-surface exposure

vocal-helper exposes the same pipeline through four coherent surfaces so it can slot into any host — a shell, a Python script, a container behind a reverse proxy, or an MCP-aware agent — without re-implementing the wiring anywhere else.

Surface	Entry point	Extra	Kind of use
argparse CLI	vocal-helper	(none — ships with the base install)	Shell scripts, cron, headless CI, pipes to jq.
click CLI	vocal-helper-click	[cli]	Rich --help, shell completion, chained sub-commands.
FastAPI HTTP	uvicorn vocal_helper.api:app	[api]	Behind a reverse proxy — upload a file, get a transcript / a full event list, GET /docs for the OpenAPI.
MCP tools	vocal-helper-mcp	[api,mcp]	Any MCP-aware host — agent runtimes, IDE integrations — publishes transcribe and pipeline as first-class tools.

# argparse
vocal-helper transcribe clip.wav --language en
vocal-helper file meeting.wav --offline --language en --llm

# click twin
vocal-helper-click transcribe clip.wav --language en

# HTTP surface
uvicorn vocal_helper.api:app --host 0.0.0.0 --port 8000 &
curl -F 'file=@clip.wav' -F 'language=en' http://localhost:8000/transcribe
curl -F 'file=@meeting.wav' -F 'llm=true'  http://localhost:8000/pipeline

# MCP surface (same FastAPI app + /mcp endpoint mounted)
vocal-helper-mcp

A one-line container recipe ships in Dockerfile — docker build -t vocal-helper . gets you an image serving both HTTP and MCP on :8000. See GUI.md for the (WIP) visual-product plan.

Subscribers — fan-out without owning the loop

Every stage can be observed without consuming the merged output stream :

async def on_voiced(seg): print("VAD:", seg["t0"], seg["t1"])
async def on_diar(seg):   print(" → ", seg["speaker"], seg["t0"], seg["t1"])

pipeline.subscribe_voiced(on_voiced)
pipeline.subscribe_diarized(on_diar)

async for ev in pipeline.run():
    ...

Useful for WebSocket / SSE relays, live UI updates, or JSONL persistence.

Diarization choice — why online cosine clustering

The pdbms study (2026-06-29, N=2089 per system) ranks the online streaming diarizers as :

Mode	Recommended	DER (clean)
Streaming ≤ 300 s	hungarian_nemo (w=20 s)	0.13 – 0.20
Streaming > 300 s	hungarian_pyannote (w=30 s)	0.30 – 0.45

Vocal Helper specialises that decision : since the VAD already isolates each voiced segment for us, the sliding-window machinery collapses to per-segment embedding + cosine-distance running-mean clustering. The default join_threshold=0.30 is the value selected on AMI dev-slice N=8 in the 2026-06-30 pyannote_stitch_threshold_sweep.

Roadmap

• v0.2 — JSONL output writer + standard WebSocket relay (mirroring capture-helper's publish path).
• v0.2 — language-locked Whisper rejection for ASR hallucinations on silence.
• v0.2 — SemanticEOTStage enabled-by-default after the 2026-06-30 EOT study validates the false-cut reduction on AMI (LiveKit-style ; see studies/eot_semantic_vs_silero.py).
• v0.2 — auto LLM-engine selector : Ollama+MLX on Apple Silicon, vLLM on Linux+NVIDIA, llama.cpp gguf on CPU fallback.
• v0.3 — out of scope : speaker ID anchoring via pre-enrolled voiceprints (excluded by user's industrial deployment compliance constraints — IDs stay anonymous S0, S1, … within a session).
• v0.3 — replace the in-stage _PyannoteEmbedder with the overlap-aware variant from pdbms.diar.backends.pyannote.embed_overlap_aware for noisy mixes.
• v0.3 — Pipecat-style typed Frame events with SystemFrame priority queue (clean shutdown / out-of-band control signals that bypass DataFrame queues).

Author

Warith HARCHAOUI — [e-mail]

Acknowledgements

Special thanks to Mohamed Chelali, Bachir Zerroug and Edmond Jacoupeau.