# Urdu S2S MVP Deployment

This is the deployable MVP path for the current Urdu S2S service:

Audio input -> Faster Whisper ASR -> OpenAI Urdu reply -> Devanagari speech text -> Chatterbox Praxy voice output.

## What Is Frozen

- ASR: `faster-whisper`
- Reply model: `OPENAI_MODEL`, default `gpt-4o-mini`
- Speech text: OpenAI-compatible chat client with Urdu-to-Devanagari prompt
- Local policy: Urdu/Hindi wording fixes, abbreviation expansion, gender guardrails
- TTS: `ResembleAI/chatterbox:v3` with Praxy anchor `bench_025.wav`
- Known non-blocking note: `bench_038` can sound like `ya` instead of `ye`

## Required Runtime

Use a CUDA GPU supported by Chatterbox's pinned Torch stack. Good choices:

- RTX 3090 / 3090 Ti
- A10 / A10G
- A100
- L40 / L40S
- H100 / H200

Avoid RTX 50-series and Blackwell RTX PRO machines for this container unless the Torch stack is upgraded, because the current Chatterbox dependency set may not support `sm_120`.

## Environment

Required:

```bash
OPENAI_API_KEY=...
```

Recommended production defaults:

```bash
OPENAI_MODEL=gpt-4o-mini
S2S_DEFAULT_MODE=live_tts
S2S_WHISPER_MODEL=small
S2S_WHISPER_DEVICE=cuda
S2S_WHISPER_COMPUTE_TYPE=float16
S2S_CHATTERBOX_DEVICE=cuda
S2S_PORT=8017
```

CPU/text-only local defaults:

```bash
S2S_DEFAULT_MODE=text_only
S2S_WHISPER_DEVICE=cpu
S2S_WHISPER_COMPUTE_TYPE=int8
```

## Local API

```bash
python3.11 -m venv .venv-deploy
source .venv-deploy/bin/activate
python -m pip install --upgrade pip wheel
python -m pip install -r requirements-api.txt -r requirements-live-s2s.txt

export OPENAI_API_KEY="..."
export OPENAI_MODEL="gpt-4o-mini"
export S2S_DEFAULT_MODE=live_tts
export S2S_WHISPER_MODEL=small
export S2S_WHISPER_DEVICE=cuda
export S2S_WHISPER_COMPUTE_TYPE=float16
export S2S_CHATTERBOX_DEVICE=cuda

PYTHONPATH=src:scripts python scripts/serve_s2s_api.py --host 0.0.0.0 --port 8017
```

Open:

```text
http://127.0.0.1:8017/
```

Health:

```bash
curl http://127.0.0.1:8017/health
```

Live request:

```bash
curl -X POST "http://127.0.0.1:8017/s2s?mode=live_tts&request_id=demo_001" \
  -F "audio=@data/processed/benchmarks/gemini_urdu_s2s_v1/audio_canonical/bench_001.wav"
```

The response includes `tts_audio_url`; fetch it from the same host.

## Docker

Build:

```bash
docker build -t urdu-s2s-mvp:latest .
```

Run on a GPU host:

```bash
docker run --gpus all --rm -p 8017:7860 \
  -e OPENAI_API_KEY="$OPENAI_API_KEY" \
  -e OPENAI_MODEL="gpt-4o-mini" \
  urdu-s2s-mvp:latest
```

Then open:

```text
http://127.0.0.1:8017/
```

## Hugging Face Space

Use a Docker Space with GPU hardware.

Billing prerequisite:

- Personal namespace: Hugging Face Pro is required for Docker Spaces.
- Organization namespace: Team/Enterprise is required for Docker Spaces.
- GPU hardware also requires prepaid credits or billing enabled.

1. Create a new Space.
2. Select SDK: Docker.
3. Push this repo or the deployment bundle.
4. Add secret `OPENAI_API_KEY`.
5. Set GPU hardware.
6. The container listens on port `7860`, which matches Hugging Face's Docker Space default.

## Vast / RunPod

Use a non-Blackwell GPU, preferably RTX 3090/A10/A100/L40S/H100.

Fastest path:

```bash
tar -xzf artifacts/live_s2s_remote_bundle.tar.gz
cd live_s2s_remote_bundle
python -m pip install --upgrade pip wheel
python -m pip install -r requirements-api.txt -r requirements-live-s2s.txt
python -m pip install torchvision==0.21.0

export OPENAI_API_KEY="..."
export S2S_DEFAULT_MODE=live_tts
export S2S_WHISPER_MODEL=small
export S2S_WHISPER_DEVICE=cuda
export S2S_WHISPER_COMPUTE_TYPE=float16
export S2S_CHATTERBOX_DEVICE=cuda

PYTHONPATH=src:scripts python scripts/serve_s2s_api.py --host 0.0.0.0 --port 8017
```

## Next Product Work

- Add auth/rate limits before public launch.
- Add request logging without storing secrets.
- Add a simple queue if concurrent TTS requests pile up.
- Later: run bigger regression batches and fix pronunciation notes.