English ASR (OpenVINO IR)
A Conformer-CTC-BPE acoustic model for English speech recognition, converted
from NVIDIA's stt_en_conformer_ctc_large
NeMo checkpoint (NeMo → ONNX → OpenVINO IR, FP16) for CPU inference.
Files
| File | Description |
|---|---|
en.xml | OpenVINO IR model topology |
en.bin | OpenVINO IR model weights (FP16) |
en.json | SentencePiece BPE vocabulary - a JSON array where each token's index is its model output id. Word-boundary marker is ▁ (U+2581). |
Blank token id (for CTC decoding) is len(vocab), i.e. one past the last
entry in en.json - it is not itself listed in the file.
Model details
- Architecture: Conformer encoder + CTC head (token classification over the vocabulary above)
- Input: log-mel spectrogram, 80 mel bins
- Frontend config (must match at inference time - NeMo's
AudioToMelSpectrogramPreprocessordefaults):- Sample rate: 16 kHz, mono
n_fft=512,hop_length=160,win_length=400, hann window- Pre-emphasis coefficient: 0.97
- Per-feature (per mel-bin) mean/std normalization after log compression
- Outputs: per-frame logits over
vocab_size + 1(vocabulary + blank) - Encoder time-subsampling: ~4x (mel frames -> output frames)
Usage
import numpy as np
import openvino as ov
core = ov.Core()
model = core.read_model("en.xml")
compiled = core.compile_model(model, "CPU")
infer_request = compiled.create_infer_request()
# mel_batch: float16 [1, 80, T] log-mel spectrogram built with the frontend
# config above; mel_length: int64 [1] = T
infer_request.infer(inputs={"audio_signal": mel_batch, "length": mel_length})
logits = infer_request.get_output_tensor(0).data # [1, T', vocab_size + 1]
ids = np.argmax(logits[0], axis=-1)
Decode with standard CTC greedy decoding: collapse consecutive duplicate
ids, then drop the blank id (len(vocab)), then map remaining ids through
en.json and replace ▁ with a space.
Intended use
Real-time and offline English dictation / transcription. Not evaluated for noisy, far-field, or multi-speaker audio. The original checkpoint's known limitations (e.g. degraded accuracy on old/low-fidelity recordings) carry over to this conversion, since the conversion preserves weights exactly.
Provenance
Converted from the original PyTorch/NeMo checkpoint via ONNX export, then OpenVINO's model conversion API to FP16 IR. Decoded output was verified to match the original NeMo model's output on held-out audio before conversion.