ksingla025's picture
Update README.md
105dcd3 verified
|
raw
history blame
1.28 kB
metadata
license: cc-by-4.0
language:
  - hi
base_model:
  - parthiv11/stt_hi_conformer_ctc_large_v2
tags:
  - speech_recognition
  - entity_tagging
  - dialect_prediction
  - gender
  - age
  - intent

This speech tagger performs transcription for Hindi, annotates key entities, predict speaker age, dialiect and intent.

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/stt_hi_conformer_ctc_entities_age_dialiect_intent'
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]}')

Dataset is from AI4Bharat IndicVoices Hindi V1 and V2 dataset.

https://indicvoices.ai4bharat.org/