NeuraSpeech_WhisperBase

<p align="center"> <img src="neura_speech_2.png" width=512 height=256 /> </p> <!-- Provide a quick summary of what the model is/does. -->

Model Description

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• Developed by: Neura company
• Funded by: Neura
• Model type: Whisper Base
• Language(s) (NLP): Persian

Model Architecture

Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It is a pre-trained model for automatic speech recognition (ASR) and speech translation.

Uses

Check out the Google Colab demo to run NeuraSpeech ASR on a free-tier Google Colab instance:

make sure these packages are installed:

from IPython.display import Audio, display
display(Audio('persian_audio.mp3', rate = 32_000,autoplay=True))

from transformers import WhisperProcessor, WhisperForConditionalGeneration
import librosa

# load model and processor
processor = WhisperProcessor.from_pretrained("Neurai/NeuraSpeech_WhisperBase")
model = WhisperForConditionalGeneration.from_pretrained("Neurai/NeuraSpeech_WhisperBase")
forced_decoder_ids = processor.get_decoder_prompt_ids(language="fa", task="transcribe")

array, sample_rate = librosa.load('persian_audio.mp3')
sr = 16000
array = librosa.to_mono(array)
array = librosa.resample(array, orig_sr=sample_rate, target_sr=16000)
input_features = processor(array, sampling_rate=sr, return_tensors="pt").input_features

# generate token ids
predicted_ids = model.generate(input_features)
# decode token ids to text
transcription = processor.batch_decode(predicted_ids,)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription)

trascribed text :

او خواهان آزاد کردن بردگان بود

More Information

https://neura.info

Model Card Authors

Esmaeil Zahedi, Mohsen Yazdinejad

Model Card Contact

[email protected]