README_EXPLAINER.md

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