nemoasr2pytorch 0.1.3

Pure-PyTorch inference port of NeMo ASR (VAD + Parakeet-TDT) for Windows/WSL

nemoasr2pytorch

Pure‑PyTorch inference port of several NeMo ASR models, with a focus on Windows / WSL support and no NeMo runtime dependency.

Currently supported:

• Frame‑level VAD: Frame_VAD_Multilingual_MarbleNet_v2.0
• ASR (RNNT‑TDT):
  • parakeet-tdt-0.6b-v2 – English
  • parakeet-tdt-0.6b-v3 – Multilingual

The project only targets inference – no training or data pipelines – and mirrors NeMo’s architecture closely so that results match NeMo as much as possible.

Installation

1. Install a suitable PyTorch + torchaudio build first (GPU or CPU), following the official instructions.
   For example, on CUDA 12.6:

   pip install torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 \
     --index-url https://download.pytorch.org/whl/cu126
   

2. Then install this package:

   pip install nemoasr2pytorch
   

Torch is not pinned as a dependency on purpose – you stay in control of the exact CUDA / CPU build.

Quick ASR usage (Parakeet‑TDT)

The simplest way to run ASR on a single WAV file:

from nemoasr2pytorch.asr.api import load_default_parakeet_tdt_model, transcribe

# lang="EN" -> parakeet-tdt-0.6b-v2 (English)
# lang="EU" -> parakeet-tdt-0.6b-v3 (multilingual)
model = load_default_parakeet_tdt_model(lang="EU")

text = transcribe(model, "your_audio.wav")
print(text)

Details:

• On first use, the corresponding .pt weights are automatically downloaded from ModelScope and cached under exports/parakeet_tdt_0.6b_v{2,3}.pt in your working directory.
• Subsequent runs reuse the local .pt directly.

Low‑precision inference (FP16 / BF16)

On GPU you can load the model directly in low precision to save memory:

from nemoasr2pytorch.asr.api import (
    load_parakeet_tdt_fp16,
    load_parakeet_tdt_bf16,
    transcribe_amp,
)

# FP16 model (GPU only)
model_fp16 = load_parakeet_tdt_fp16(lang="EU")
print("FP16:", transcribe_amp(model_fp16, "your_audio.wav"))

# BF16 model (if hardware supports it)
model_bf16 = load_parakeet_tdt_bf16(lang="EU")
print("BF16:", transcribe_amp(model_bf16, "your_audio.wav"))

transcribe_amp uses PyTorch AMP (torch.amp.autocast) on CUDA to run the model in mixed precision.

VAD (MarbleNet) for pre‑segmentation

Frame‑level VAD API:

from nemoasr2pytorch.vad.api import load_default_frame_vad_model, run_vad_on_waveform
import torchaudio

# Loads MarbleNet VAD; if the .pt is missing, it is auto-downloaded
# from ModelScope to ./exports/frame_vad_multilingual_marblenet_v2.0.pt
vad_model = load_default_frame_vad_model()

waveform, sr = torchaudio.load("your_audio.wav")
if sr != vad_model.preprocessor.sample_rate:
    waveform = torchaudio.functional.resample(
        waveform, sr, vad_model.preprocessor.sample_rate
    )

probs, segments = run_vad_on_waveform(vad_model, waveform.squeeze(0))
print("Segments:", segments)

Long‑audio inference (concept)

The repository version ships a reference script inference.py which:

• loads a Parakeet model (v2/v3, chosen by lang);
• optionally runs MarbleNet VAD to detect speech regions;
• merges VAD segments into chunks based on min_seg / max_seg length;
• runs Parakeet on each chunk and concatenates the results.

The core logic is implemented via the public APIs:

• nemoasr2pytorch.vad.api – VAD model + run_vad_on_waveform
• nemoasr2pytorch.asr.api – Parakeet model + transcribe / transcribe_amp

You can either:

• copy inference.py from the GitHub repo and adapt it to your own CLI; or
• re‑implement a similar pipeline in your application using the two APIs above.

Package APIs

Main public modules:

• nemoasr2pytorch.asr.api

  • load_default_parakeet_tdt_model(lang="EN" | "EU", device=None, dtype=torch.float32)
    Load Parakeet‑TDT in FP32; lang chooses v2 (EN) vs v3 (EU).
  • load_parakeet_tdt_fp16(lang="EN" | "EU", device=None)
    Load FP16 model (usually on GPU).
  • load_parakeet_tdt_bf16(lang="EN" | "EU", device=None)
    Load BF16 model (if supported).
  • transcribe(model, audio)
    Greedy TDT decoding in full precision (CPU or GPU).
  • transcribe_amp(model, audio)
    Greedy TDT decoding with AMP on CUDA for low‑precision models.

• nemoasr2pytorch.vad.api

  • load_default_frame_vad_model(device=None, dtype=torch.float32)
    Load the MarbleNet VAD model from a local .pt.
  • run_vad_on_waveform(model, audio, ...)
    Compute per‑frame speech probabilities and return merged speech segments.

Notes / Limitations

• This package focuses on inference only; training and NeMo’s full config stack (Hydra/Lightning) are intentionally omitted.
• Parakeet weights (.pt) are auto‑downloaded from ModelScope on first use; VAD .pt is currently expected to be provided by the user (converted from NeMo).
• For best performance and lower memory usage, a CUDA‑enabled PyTorch build is recommended; CPU‑only inference also works but will be slower on long audio.