Upload README.md

README.md

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+---
+language:
+- en
+- es
+- fr
+- it
+- de
+- pt
+library_name: nemo
+datasets:
+- mozilla-foundation/common_voice_8_0
+- MLCommons/peoples_speech
+- librispeech_asr
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- FastConformer
+- Conformer
+- pytorch
+- NeMo
+- hf-asr-leaderboard
+- ctc
+- entity-tagging
+- speaker-attributes
+license: cc-by-4.0
+---
+
+# Meta ASR English 
+
+This model is a fine-tuned version of NVIDIA's Parakeet CTC 0.6B model, enhanced with entity tagging, speaker attributes, and multi-language support for European languages.
+
+## Model Details
+
+- **Base Model**: Parakeet CTC 0.6B (FastConformer)
+- **Fine-tuned on**: Mix of CommonVoice (6 European languages), People's Speech, Indian accented English, and LibriSpeech
+- **Languages**: English, Spanish, French, Italian, German, Portuguese
+- **Additional Features**: Entity tagging, speaker attributes (age, gender, emotion), and intent detection
+
+## Output Format
+
+The model provides rich transcriptions including:
+- Entity tags (PERSON_NAME, ORGANIZATION, etc.)
+- Speaker attributes (AGE, GENDER, EMOTION)
+- Intent classification
+- Language-specific transcription
+
+Example output:
+```
+ENTITY_PERSON_NAME Robert Hoke END was educated at the ENTITY_ORGANIZATION Pleasant Retreat Academy END. AGE_45_60 GER_MALE EMOTION_NEUTRAL INTENT_INFORM
+```
+
+## Usage
+
+```python
+import nemo.collections.asr as nemo_asr
+
+# Load model
+asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained('WhissleAI/meta_stt_euro_v1')
+
+# Transcribe audio
+transcription = asr_model.transcribe(['path/to/audio.wav'])
+print(transcription[0])
+```
+
+## Training Data
+
+The model was fine-tuned on:
+- CommonVoice dataset (6 European languages)
+- People's Speech English corpus
+- Indian accented English
+- LibriSpeech corpus (en, es, fr, it, pt)
+
+## Model Architecture
+
+Based on FastConformer [1] architecture with 8x depthwise-separable convolutional downsampling, trained using CTC loss.
+
+## License
+
+This model is licensed under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
+
+## References
+
+[1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)
+[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)