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

https://neura.info

Model Card Authors

Esmaeil Zahedi, Mohsen Yazdinejad

Model Card Contact

info@neura.info