🎙️ Tawasul STT V0 (Supports all Arabic Dialects with more focus on Egyptian Arz dialect)

|

|

|

|

🏗️ Model Architecture

📥 Input

• Input Type: Audio
• Input Format(s): .wav files
• Other Properties Related to Input: 16000 Hz Mono-channel Audio, Pre-Processing Not Needed

📤 Output

• Output Type: Text
• Output Format: String
• Output Parameters: One Dimensional (1D)
• Other Properties Related to Output: May Need Inverse Text Normalization; Does Not Handle Special Characters; Outputs text in Arabic without diacritical marks

⚠️ Limitations

• The model is non-streaming and outputs the speech as a string without diacritical marks.
• Not recommended for word-for-word transcription and punctuation as accuracy varies based on the characteristics of input audio (unrecognized word, accent, noise, speech type, and context of speech).

🚀 How to download and use the model

🔧 Installations

$ apt-get update && apt-get install -y libsndfile1 ffmpeg soundfile librosa sentencepiece Cython packaging 
$ pip -q install nemo_toolkit['asr']

📥 Download the model

🐍 Imports and usage

import nemo.collections.asr as nemo_asr

asr_model = nemo_asr.models.ASRModel.restore_from(
    "path/to/tawasul_egy_stt.nemo",
)

🎯 Transcribing using Python

prediction = asr_model.transcribe(['sample_audio_to_transcribe.wav'])
print(prediction.text)

📊 Training, and Testing Datasets

🏋️ Training Datasets

The model is trained by Nvidia on a composite dataset comprising around 760 hours of Arabic speech:

• Massive Arabic Speech Corpus (MASC) [690h]
  • Data Collection Method: Automated
  • Labeling Method: Automated
• Mozilla Common Voice 17.0 Arabic [65h]
  • Data Collection Method: by Human
  • Labeling Method: by Human
• Google Fleurs Arabic [5h]
  • Data Collection Method: by Human
  • Labeling Method: by Human

And then the model was further finetuned on around 100 hours of PRIVATE Egyptian dialects Arabic speech

• The second stage training Egyptian data is Private, there is no intention to open-source the data

🧪 Test Benchmark datasets

📈 Test Benchmark results

• CommonVoice
  • WER:
  • CER:
• MASC
  • Clean
    • WER:
    • CER:
  • Noisy
    • WER:
    • CER:
• MGB-2
  • WER:
  • CER:
• Casablanca
  • WER:
  • CER:
• SADA
  • WER:
  • CER:

🔧 Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere
• NVIDIA Blackwell
• NVIDIA Jetson
• NVIDIA Hopper
• NVIDIA Lovelace
• NVIDIA Pascal
• NVIDIA Turing
• NVIDIA Volta

⚙️ Runtime Engine

• Nemo 2.0.0

🖥️ Preferred Operating System

• Linux

🔍 Explainability

• High-Level Application and Domain: Automatic Speech Recognition
• • Describe how this model works: The model transcribes audio input into text for the Arabic language
• Verified to have met prescribed quality standards: Yes
• Performance Metrics: Word Error Rate (WER), Character Error Rate (CER), Real-Time Factor
• Potential Known Risks: Transcripts may not be 100% accurate. Accuracy varies based on the characteristics of input audio (Domain, Use Case, Accent, Noise, Speech Type, Context of speech, etcetera).

⚖️ Bias

• Was the model trained with a specific accent? The model was trained on general Arabic Dialects and then further fine-tuned on Egyptian dialect (Arz)
• Have any special measures been taken to mitigate unwanted bias? No

🔒 Safety & Security

Use Case Restrictions:

• Non-streaming ASR model
• Model outputs text in Arabic without diacritical marks
• Output text requires Inverse Text Normalization
• The model is noise-sensitive
• The model is Egyptian Dialect further finetuned

📄 License

📚 References