WhissleAI/stt_en_conformer_ctc_large_with_meta

This speech tagger performs transcription, annotates entities, predict speaker emotion

Model is suitable for voiceAI applications, real-time and offline.

Model Details

• Model type: NeMo ASR
• Architecture: Conformer CTC
• Language: English
• Training data: CommonVoice, Gigaspeech
• Performance metrics: [Metrics]

Usage

To use this model, you need to install the NeMo library:

pip install nemo_toolkit

How to run

import nemo.collections.asr as nemo_asr

# Step 1: Load the ASR model from Hugging Face
model_name = 'WhissleAI/speech-tagger_en_ner_emotion'
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name)

# Step 2: Provide the path to your audio file
audio_file_path = '/path/to/your/audio_file.wav'

# Step 3: Transcribe the audio
transcription = asr_model.transcribe(paths2audio_files=[audio_file_path])
print(f'Transcription: {transcription[0]}')