Add stt_hy_fastconformer_hybrid_large_pc model
Browse filesSigned-off-by: Alexan <[email protected]>
- .gitattributes +1 -0
- README.md +149 -2
- stt_hy_fastconformer_hybrid_large_pc.nemo +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.nemo filter=lfs diff=lfs merge=lfs -text
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README.md
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license: cc-by-4.0
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license: cc-by-4.0
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datasets:
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- mozilla-foundation/common_voice_17_0
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- google/fleurs
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language:
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- hy
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pipeline_tag: automatic-speech-recognition
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library_name: NeMo
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metrics:
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- WER
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- CER
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tags:
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- speech-recognition
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- ASR
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- Armenian
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- Conformer
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- Transducer
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- CTC
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- NeMo
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- hf-asr-leaderboard
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- speech
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- audio
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model-index:
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- name: stt_hy_fastconformer_hybrid_large_pc
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: MCV17
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type: mozilla-foundation/common_voice_17_0
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split: test
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args:
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language: hy
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metrics:
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- name: Test WER
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type: wer
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value: 9.90
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: FLEURS
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type: google/fleurs
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split: test
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args:
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language: hy
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metrics:
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- name: Test WER
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type: wer
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value: 12.32
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model-details:
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name: NVIDIA FastConformer-Hybrid Large (hy)
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description: |
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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.
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license: cc-by-4.0
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architecture: FastConformer-Hybrid
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tokenizer:
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type: SentencePiece
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vocab_size: 1024
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inputs:
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type: audio
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format: wav
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properties:
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- 16000 Hz Mono-channel Audio
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- Pre-Processing Not Needed
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outputs:
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type: text
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format: string
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properties:
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- Armenian text with punctuation and capitalization
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- May need inverse text normalization
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- Does not handle special characters
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limitations:
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- Non-streaming model
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- Accuracy depends on input audio characteristics
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- Not recommended for word-for-word transcription
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- Limited domain-specific vocabulary
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usage:
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framework: NeMo
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pre-trained-model: nvidia/stt_hy_fastconformer_hybrid_large_pc
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code:
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- import nemo.collections.asr as nemo_asr
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- asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name="nvidia/stt_hy_fastconformer_hybrid_large_pc")
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- asr_model.transcribe(['your_audio_file.wav'])
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training:
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epochs: 200
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dataset:
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total_hours: 296.19
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sources:
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- Mozilla Common Voice 17.0 (48h)
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- Google Fleurs (12h)
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- ArmenianGrqaserAudioBooks (21.96h)
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- Proprietary Corpus 1 (69.23h)
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- Proprietary Corpus 2 (145h)
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evaluation:
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datasets:
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- Mozilla Common Voice 17.0
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- Google Fleurs
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- Proprietary Corpus 1
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metrics:
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WER:
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- MCV Test WER: 9.90
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- FLEURS Test WER: 12.32
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CER: Not provided
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deployment:
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hardware:
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- NVIDIA Ampere
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- NVIDIA Blackwell
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- NVIDIA Jetson
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- NVIDIA Hopper
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- NVIDIA Lovelace
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- NVIDIA Pascal
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- NVIDIA Turing
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- NVIDIA Volta
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runtime: NeMo 2.0.0
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os: Linux
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ethical-considerations:
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trustworthy-ai:
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considerations: Ensure model meets requirements for relevant industries and addresses misuse.
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explainability:
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application: Automatic Speech Recognition
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performance:
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- WER
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- CER
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- Real-Time Factor
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risks:
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- Accuracy may vary with input characteristics.
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privacy:
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compliance: Reviewed for privacy laws
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personal-data: No identifiable personal data
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safety:
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use-cases: Not applicable for life-critical applications.
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noise-sensitivity: Sensitive to noise and input variations.
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stt_hy_fastconformer_hybrid_large_pc.nemo
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:b75fb9203d48c1a50db3ab6890df0f3d85086a14d9547940adac69c1deaa20eb
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size 459243520
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