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Whissle STT-meta-1B
Multilingual speech recognition model with dual-head tag classifier for real-time speaker metadata extraction. Built on Conformer-CTC architecture with 18 encoder layers. Supports 9 languages with age, gender, emotion, and intent detection per utterance.
Model Details
| Architecture | Conformer-CTC + dual-head tag classifier |
| Encoder | 512-dim, 18 layers, 4x subsampling |
| Download size | ~488 MB |
| Format | NeMo (.nemo) and ONNX (CPU and GPU compatible) |
| Sample rate | 16 kHz mono |
| Languages | English, Hindi, Spanish, French, German, Italian, Gujarati, Marathi |
| Base model | nvidia/parakeet-ctc-0.6b |
Tag Classifier Outputs
| Category | Classes | Labels |
|---|---|---|
| Age | 6 | 0-18, 18-30, 30-45, 45-60, 60+, NONE |
| Emotion | 8 | NEUTRAL, HAPPY, SAD, ANGRY, FEAR, SURPRISE, DISGUST, NONE |
| Gender | 4 | MALE, FEMALE, OTHER, NONE |
| Intent | 10 | COMMAND, DESCRIBE, EXCLAIM, EXPLAIN, INFORM, OPINION, QUESTION, REQUEST, STATEMENT, NONE |
Quick Start
Use with the Whissle STT Inference Server (ONNX, CPU):
git clone https://github.com/WhissleAI/whissle_stt_inference.git
cd whissle_stt_inference
./setup.sh --model en-meta
Or load directly with NeMo:
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained("WhissleAI/STT-meta-1B")
transcriptions = asr_model.transcribe(["/path/to/your/audio.wav"])
Also usable with PromptingNemo.
Performance
Tested on CPU (Apple M-series):
| Audio length | Inference time | RTF | Tags |
|---|---|---|---|
| 25.9s | 3.6s | 0.14x | Female, 30-45, Neutral, Describe |
| 1.1s | 0.46s | 0.42x | Female, 18-30, Happy, Question |
License
Whissle Inference-Only License — inference only, no training/fine-tuning/distillation/reverse engineering. Free under 100M MAU.
Citation
@misc{whissle2026sttmeta1b,
title={Whissle STT-meta-1B: Multilingual ASR with Intent, Emotion, and Voice Biometrics},
author={Whissle AI},
year={2026},
url={https://huggingface.co/WhissleAI/STT-meta-1B}
}
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Base model
nvidia/parakeet-ctc-0.6b