metadata
library_name: Nvidia Nemo
license: apache-2.0
language:
- fa
pipeline_tag: automatic-speech-recognition
tags:
- Persian
- Neura
- PersianASR
datasets:
- common_voice_17_0
Neura Speech Nemo
Model Description
- Developed by: Neura company
- Funded by: Neura
- Model type: fa_FastConformers_Transducer
- Language(s) (NLP): Persian
Model Architecture
This model uses a FastConformer-TDT architecture. FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. You may find more information on the details of FastConformer here: Fast-Conformer Model. Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition.
Uses
Check out the Google Colab demo to run NeuraSpeech ASR on a free-tier Google Colab instance: 
make sure these packages are installed:
!pip install nemo_toolkit['all']
from IPython.display import Audio, display
display(Audio('persian_audio.mp3', rate = 32_000,autoplay=True))
import nemo
print('nemo', nemo.__version__)
import numpy as np
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="Neurai/NeuraSpeech_900h")
asr_model.transcribe(paths2audio_files=['persian_audio.mp3', ], batch_size=1)[0]
trascribed text :
او خواهان آزاد کردن بردگان بود
More Information
Model Card Authors
Esmaeil Zahedi, Mohsen Yazdinejad