| # README_EXPLAINER |
| |
| Purpose: give strong project context to humans and Copilot agents running on a RunPod pod. |
| |
| ## Project summary |
| |
| This project hosts an Indic multilingual TTS model behind an HTTP API, with a small input surface for end users: |
| - utterance |
| - language |
| - user_id |
|
|
| The service applies tuned inference defaults server-side and returns generated audio. |
|
|
| ## Source model and pipeline context |
|
|
| Base model details: |
| - Model lineage is based on `snorbyte/snorTTS-Indic-v0` |
| - Hosted checkpoint currently used by this project: `Mevearth2/Quantized-Merged-TTS` |
| - Architecture family: LLaMA-style causal LM generating audio tokens |
| - Audio decode backend: SNAC (`hubertsiuzdak/snac_24khz`) |
|
|
| High-level generation flow: |
| 1. Build prompt with language + speaker id + utterance |
| 2. Generate SNAC token ids with the LM |
| 3. Convert token stream into SNAC codebooks |
| 4. Decode to 24 kHz waveform |
| 5. Apply optional post-process (speed, denoise) |
|
|
| Prompt format used: |
| `<custom_token_3><|begin_of_text|>{language}{user_id}: {utterance}<|eot_id|><custom_token_4><custom_token_5><custom_token_1>` |
|
|
| ## Data context (important for future finetuning) |
|
|
| Training dataset reference: |
| - HF dataset: `snorbyte/indic-tts-sample-snac-encoded` |
|
|
| Observed splits: |
| - `stage_1` |
| - `stage_2` |
| - `eval` |
|
|
| Common columns in all splits: |
| - utterance |
| - language |
| - emotion |
| - type |
| - act |
| - rating |
| - gender |
| - age |
| - environment |
| - user |
| - snac_codes |
| - stage |
| |
| Language set: |
| - hindi |
| - tamil |
| - telugu |
| - marathi |
| - kannada |
| - malayalam |
| - punjabi |
| - gujarati |
| - bengali |
| |
| Note for retraining work: |
| - The hosted dataset split names are not `train/valid/test`; scripts should use `stage_1`, `stage_2`, `eval` explicitly. |
| |
| ## Speaker mapping context |
| |
| This project includes language-speaker validation and recommended speed defaults in `app/speaker_map.py`. |
|
|
| Current practical mapping by language: |
| - hindi: 159, 49, 43 |
| - tamil: 188, 128, 176 |
| - bengali: 125 |
| - malayalam: 189, 124 |
| - kannada: 142, 138, 131, 59 |
| - telugu: 69, 133 |
| - punjabi: 191, 67, 201 |
| - gujarati: 62, 190 |
| - marathi: 205, 82, 199, 203 |
|
|
| ## What has already been implemented |
|
|
| Production starter API: |
| - `app/main.py` |
| - `POST /v1/tts` (inputs: utterance, language, user_id) |
| - `GET /v1/options` (dropdown data for UI) |
| - `GET /health` |
| - `GET /ready` |
| |
| Inference runtime: |
| - `app/runtime.py` |
| - one-time model load |
| - runtime defaults for generation |
| - prompt construction |
| - token-to-audio decode |
| - wav bytes response |
| |
| Schemas: |
| - `app/schemas.py` |
| |
| Speaker map and validation: |
| - `app/speaker_map.py` |
|
|
| Load test starter: |
| - `loadtest/locustfile.py` |
| - `loadtest/requirements.txt` |
|
|
| RunPod no-docker scripts: |
| - `scripts/runpod_setup.sh` |
| - `scripts/runpod_start.sh` |
|
|
| ## Important environment variables |
|
|
| Main: |
| - `MODEL_NAME` (default `Mevearth2/Quantized-Merged-TTS`) |
| - `HF_TOKEN` |
|
|
| Service behavior: |
| - `MAX_INFLIGHT_REQUESTS` |
| - `TTS_TEMPERATURE` |
| - `TTS_TOP_P` |
| - `TTS_REPETITION_PENALTY` |
| - `TTS_MAX_SEQ_LENGTH` |
| - `TTS_MAX_WORDS` |
| - `TTS_DENOISE` |
|
|
| Template file: |
| - `.env.example` (no secrets) |
| Actual runtime secrets: |
| - `.env` (do not commit) |
|
|
| ## Why no-docker path exists |
|
|
| Docker image builds were unstable due to slow network/timeouts while downloading large Python wheels in some environments. |
|
|
| Current recommended fast path: |
| 1. Open RunPod PyTorch pod |
| 2. Clone `tts_hosting` into `/workspace/tts_hosting` |
| 3. Run `scripts/runpod_setup.sh` |
| 4. Run `scripts/runpod_start.sh` |
|
|
| ## Current known constraints |
|
|
| - Generation can be slow for long utterances, so word limits are enforced. |
| - Denoise dependencies are intentionally optional to reduce deployment friction. |
| - 5090 GPUs require newer PyTorch builds; verify pod torch compatibility first. |
|
|
| ## What to do next (execution plan) |
|
|
| Phase 1: Stabilize endpoint |
| 1. Deploy via no-docker RunPod path |
| 2. Verify `/ready` and sample `/v1/tts` calls |
| 3. Confirm language-user validation behavior |
|
|
| Phase 2: Frontend readiness |
| 1. Use `/v1/options` for language and speaker dropdowns |
| 2. Keep only 3 user inputs in UI |
| 3. Keep generation knobs hidden on backend defaults |
|
|
| Phase 3: Load testing |
| 1. Run Locust against pod URL |
| 2. Sweep concurrency: 1, 2, 4, 8, 12 |
| 3. Track p50/p95/p99 latency and error rate |
| 4. Set stable `MAX_INFLIGHT_REQUESTS` |
|
|
| Phase 4: Production hardening |
| 1. Add API authentication |
| 2. Add structured logs and metrics |
| 3. Add queue/backpressure policy and request timeout policy |
| 4. Add autoscaling strategy and cost-per-request reporting |
|
|
| ## For Copilot agents on pod |
|
|
| When helping on this repo, prioritize: |
| 1. Reliability over feature creep |
| 2. Keeping API input surface simple |
| 3. Preserving speaker/language validation |
| 4. Avoiding dependency bloat unless requested |
| 5. Not committing secrets from `.env` |
|
|
| If modifying runtime behavior, always keep: |
| - prompt format compatibility |
| - speaker mapping checks |
| - deterministic server defaults unless explicitly changed |
|
|
