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--- |
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library_name: transformers |
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tags: |
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- persian |
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- whisper-base |
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- whisper |
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- farsi |
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- Neura |
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- NeuraSpeech |
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license: apache-2.0 |
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language: |
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- fa |
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pipeline_tag: automatic-speech-recognition |
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--- |
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# |
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<p align="center"> |
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<img src="neura_speech_2.png" width=512 height=256 /> |
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</p> |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Neura company |
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- **Funded by:** Neura |
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- **Model type:** Whisper Base |
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- **Language(s) (NLP):** Persian |
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## Model Architecture |
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This model uses a FastConformer-TDT architecture. FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. |
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You may find more information on the details of FastConformer here: Fast-Conformer Model. |
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[Fast Conformer with Linearly Scalable Attention for Efficient |
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Speech Recognition](https://arxiv.org/abs/2305.05084). |
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## Uses |
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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: |
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```python |
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from IPython.display import Audio, display |
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display(Audio('persian_audio.mp3', rate = 32_000,autoplay=True)) |
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``` |
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```python |
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from transformers import WhisperProcessor, WhisperForConditionalGeneration |
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import librosa |
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# load model and processor |
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processor = WhisperProcessor.from_pretrained("Neurai/NeuraSpeech_WhisperBase") |
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model = WhisperForConditionalGeneration.from_pretrained("Neurai/NeuraSpeech_WhisperBase") |
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forced_decoder_ids = processor.get_decoder_prompt_ids(language="fa", task="transcribe") |
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array, sample_rate = librosa.load('persian_audio.mp3', sr=16000,mono=True) |
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sr = 16000 |
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array = librosa.to_mono(array) |
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array = librosa.resample(array, orig_sr=sample_rate, target_sr=16000) |
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input_features = processor(array, sampling_rate=sr, return_tensors="pt").input_features |
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# generate token ids |
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predicted_ids = model.generate(input_features) |
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# decode token ids to text |
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transcription = processor.batch_decode(predicted_ids,) |
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) |
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print(transcription) |
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``` |
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trascribed text : |
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``` |
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او خواهان آزاد کردن بردگان بود |
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``` |
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## More Information |
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https://neura.info |
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## Model Card Authors |
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Esmaeil Zahedi, Mohsen Yazdinejad |
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## Model Card Contact |
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[email protected] |
