| license: cc-by-4.0 | |
| datasets: | |
| - mozilla-foundation/common_voice_17_0 | |
| - google/fleurs | |
| language: | |
| - hy | |
| pipeline_tag: automatic-speech-recognition | |
| library_name: NeMo | |
| metrics: | |
| - WER | |
| - CER | |
| tags: | |
| - speech-recognition | |
| - ASR | |
| - Armenian | |
| - Conformer | |
| - Transducer | |
| - CTC | |
| - NeMo | |
| - hf-asr-leaderboard | |
| - speech | |
| - audio | |
| model-index: | |
| - name: stt_hy_fastconformer_hybrid_large_pc | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: MCV17 | |
| type: mozilla-foundation/common_voice_17_0 | |
| split: test | |
| args: | |
| language: hy | |
| metrics: | |
| - name: Test WER | |
| type: wer | |
| value: 9.90 | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: FLEURS | |
| type: google/fleurs | |
| split: test | |
| args: | |
| language: hy | |
| metrics: | |
| - name: Test WER | |
| type: wer | |
| value: 12.32 | |
| model-details: | |
| name: NVIDIA FastConformer-Hybrid Large (hy) | |
| description: | | |
| This model transcribes speech in the Armenian language with capitalization and punctuation marks support. It is a "large" version of the FastConformer Transducer-CTC model with 115M parameters, trained on Transducer (default) and CTC losses. | |
| license: cc-by-4.0 | |
| architecture: FastConformer-Hybrid | |
| tokenizer: | |
| type: SentencePiece | |
| vocab_size: 1024 | |
| inputs: | |
| type: audio | |
| format: wav | |
| properties: | |
| - 16000 Hz Mono-channel Audio | |
| - Pre-Processing Not Needed | |
| outputs: | |
| type: text | |
| format: string | |
| properties: | |
| - Armenian text with punctuation and capitalization | |
| - May need inverse text normalization | |
| - Does not handle special characters | |
| limitations: | |
| - Non-streaming model | |
| - Accuracy depends on input audio characteristics | |
| - Not recommended for word-for-word transcription | |
| - Limited domain-specific vocabulary | |
| usage: | |
| framework: NeMo | |
| pre-trained-model: nvidia/stt_hy_fastconformer_hybrid_large_pc | |
| code: | |
| - import nemo.collections.asr as nemo_asr | |
| - asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name="nvidia/stt_hy_fastconformer_hybrid_large_pc") | |
| - asr_model.transcribe(['your_audio_file.wav']) | |
| training: | |
| epochs: 200 | |
| dataset: | |
| total_hours: 296.19 | |
| sources: | |
| - Mozilla Common Voice 17.0 (48h) | |
| - Google Fleurs (12h) | |
| - ArmenianGrqaserAudioBooks (21.96h) | |
| - Proprietary Corpus 1 (69.23h) | |
| - Proprietary Corpus 2 (145h) | |
| evaluation: | |
| datasets: | |
| - Mozilla Common Voice 17.0 | |
| - Google Fleurs | |
| - Proprietary Corpus 1 | |
| metrics: | |
| WER: | |
| - MCV Test WER: 9.90 | |
| - FLEURS Test WER: 12.32 | |
| CER: Not provided | |
| deployment: | |
| hardware: | |
| - NVIDIA Ampere | |
| - NVIDIA Blackwell | |
| - NVIDIA Jetson | |
| - NVIDIA Hopper | |
| - NVIDIA Lovelace | |
| - NVIDIA Pascal | |
| - NVIDIA Turing | |
| - NVIDIA Volta | |
| runtime: NeMo 2.0.0 | |
| os: Linux | |
| ethical-considerations: | |
| trustworthy-ai: | |
| considerations: Ensure model meets requirements for relevant industries and addresses misuse. | |
| explainability: | |
| application: Automatic Speech Recognition | |
| performance: | |
| - WER | |
| - CER | |
| - Real-Time Factor | |
| risks: | |
| - Accuracy may vary with input characteristics. | |
| privacy: | |
| compliance: Reviewed for privacy laws | |
| personal-data: No identifiable personal data | |
| safety: | |
| use-cases: Not applicable for life-critical applications. | |
| noise-sensitivity: Sensitive to noise and input variations. | |