| --- |
| library_name: transformers |
| tags: |
| - persian |
| - whisper-base |
| - whisper |
| - farsi |
| - Neura |
| - NeuraSpeech |
| license: apache-2.0 |
| language: |
| - fa |
| pipeline_tag: automatic-speech-recognition |
| --- |
| |
|
|
| # |
|
|
| <p align="center"> |
| <img src="neura_speech_2.png" width=512 height=256 /> |
| </p> |
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| <!-- Provide a quick summary of what the model is/does. --> |
|
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| ## Model Description |
|
|
| <!-- Provide a longer summary of what this model is. --> |
|
|
| - **Developed by:** Neura company |
| - **Funded by:** Neura |
| - **Model type:** Whisper Base |
| - **Language(s) (NLP):** Persian |
|
|
| ## Model Architecture |
|
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| 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: [](https://colab.research.google.com/drive/12d7zecB94ah7ZHKnDtJF58saLzdkZAj3#scrollTo=oNt032WVkQUa) |
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| make sure these packages are installed: |
|
|
| ```python |
| from IPython.display import Audio, display |
| display(Audio('persian_audio.mp3', rate = 32_000,autoplay=True)) |
| ``` |
|
|
| ```python |
| 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 |
| info@neura.info |
