vllm-omni/examples/offline_inference/audex/text_to_speech.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Offline Audex (Nemotron-Labs-Audex-2B) TTS inference example.

Runs Stage 0 (thinker) + Stage 1 (streaming causal speech decoder) end-to-end
through the vLLM-Omni engine and saves a 16 kHz mono WAV per prompt.

Pass the HF repo ROOT as --model; per-stage subfolders resolve automatically.

Examples:

    python examples/offline_inference/audex/text_to_speech.py \\
        --texts "The weather is so good today." \\
        --output-dir results/audex_wavs

    # TSV corpus (utt_id<TAB>text per line), all requests in flight at once:
    python examples/offline_inference/audex/text_to_speech.py \\
        --texts-file corpus.tsv --output-dir results/audex_wavs

    # One request at a time:
    python examples/offline_inference/audex/text_to_speech.py \\
        --texts-file corpus.tsv --sequential --output-dir results/audex_wavs
"""

from __future__ import annotations

import argparse
import time
from pathlib import Path

from common import (
    GUIDED_TEMPERATURE,
    SAMPLE_RATE,
    extract_pcm,
    guided_stage_params,
    load_audex_tokenizer,
    load_corpus,
    request_index,
    write_wav,
)

from vllm_omni import Omni
from vllm_omni.model_executor.models.audex.prompt import build_cond_prompt

DEFAULT_TEXTS = (
    "Hello world.",
    "The quick brown fox jumps over the lazy dog.",
    "Today is a beautiful day for a walk in the park.",
)


def parse_args():
    parser = argparse.ArgumentParser(description="Offline Audex TTS inference")
    parser.add_argument(
        "--model",
        type=str,
        default="nvidia/Nemotron-Labs-Audex-2B",
        help="HF repo root (or local snapshot root).",
    )
    parser.add_argument("--texts", type=str, nargs="+", default=None, help="Plain-text prompts.")
    parser.add_argument(
        "--texts-file",
        type=str,
        default=None,
        help="TSV corpus: one 'utt_id<TAB>text' per line (overrides --texts).",
    )
    parser.add_argument("--output-dir", type=str, default="results/audex_wavs")
    parser.add_argument("--deploy-config", type=str, default=None, help="Override the deploy config path.")
    parser.add_argument(
        "--sequential",
        action="store_true",
        help="Submit one request at a time instead of all at once.",
    )
    parser.add_argument(
        "--cfg-scale",
        type=float,
        default=1.0,
        help=(
            "Classifier-free guidance strength. 1.0 disables guidance (default, "
            "matches the v1 baseline); 1.5 is the official quality setting. "
            "Guided runs submit one request at a time (each carries its own "
            "length-matched null prompt and pair id)."
        ),
    )
    parser.add_argument(
        "--temperature",
        type=float,
        default=None,
        help="Override the deploy yaml's stage-0 sampling temperature.",
    )
    return parser.parse_args()


def _write_outputs(outputs, batch: list[tuple[str, str]], output_dir: Path) -> float:
    ordered = sorted(outputs, key=request_index)
    if len(ordered) != len(batch):
        raise RuntimeError(f"expected {len(batch)} outputs, got {len(ordered)}")

    total_dur = 0.0
    for (utt, _text), req_output in zip(batch, ordered):
        pcm = extract_pcm(req_output.outputs[0].multimodal_output)
        dur = pcm.numel() / SAMPLE_RATE
        if dur <= 0:
            raise RuntimeError(f"empty audio for {utt}")
        out_path = output_dir / f"{utt}.wav"
        write_wav(out_path, pcm)
        total_dur += dur
        print(f"  {utt:<40} dur={dur:6.2f}s  -> {out_path}")
    return total_dur


def main():
    args = parse_args()
    output_dir = Path(args.output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    corpus = load_corpus(args.texts_file, args.texts, DEFAULT_TEXTS)

    engine = Omni(model=args.model, deploy_config=args.deploy_config, trust_remote_code=True)

    cfg_enabled = args.cfg_scale > 1.0
    tokenizer = load_audex_tokenizer(args.model) if cfg_enabled else None

    print(f"Model       : {args.model}")
    print(f"Prompts     : {len(corpus)}")
    print(f"Mode        : {'sequential' if args.sequential or cfg_enabled else 'batched'}")
    print(f"CFG scale   : {args.cfg_scale}" + (" (guidance off)" if not cfg_enabled else ""))
    print(f"Output dir  : {output_dir}")

    total_elapsed = 0.0
    total_dur = 0.0
    # CFG runs one request at a time: each request needs its own pair id and
    # text-specific null prompt in the stage-0 sampling params.
    batches = [[item] for item in corpus] if (args.sequential or cfg_enabled) else [corpus]
    for batch in batches:
        prompts = [build_cond_prompt(text) for _utt, text in batch]
        sampling_params_list = None
        if cfg_enabled:
            sampling_params_list = guided_stage_params(
                engine,
                prompts[0],
                tokenizer,
                args.cfg_scale,
                f"cfg-{batch[0][0]}",
                temperature=GUIDED_TEMPERATURE,
            )
        if args.temperature is not None:
            import copy

            if sampling_params_list is None:
                sampling_params_list = copy.deepcopy(engine.resolve_sampling_params_list(None))
            sampling_params_list[0].temperature = args.temperature
        t_start = time.perf_counter()
        outputs = engine.generate(prompts, sampling_params_list)
        total_elapsed += time.perf_counter() - t_start
        total_dur += _write_outputs(outputs, batch, output_dir)

    rtf = total_elapsed / total_dur if total_dur > 0 else float("inf")
    print(f"Total infer : {total_elapsed:.2f}s  total audio: {total_dur:.2f}s  RTF: {rtf:.3f}")


if __name__ == "__main__":
    main()