TawasulAI
/

tawasul-egy-stt

@@ -40,7 +40,6 @@ The model transcribes text in Arabic without diacritical marks and supports peri
 This model is ready for commercial and non-commercial use. ✅
 ## 🏗️ Model Architecture
 FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling.
 The model is trained in a multitask setup with hybrid Transducer decoder (RNNT) and Connectionist Temporal Classification (CTC) loss.
 You may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer).
@@ -53,7 +52,6 @@ Model utilizes a [Google Sentencepiece Tokenizer](https://github.com/google/sent
   - **Other Properties Related to Input:** 16000 Hz Mono-channel Audio, Pre-Processing Not Needed
 ### 📤 Output
 This model provides transcribed speech as a string for a given audio sample.
   - **Output Type**: Text
   - **Output Format:** String
@@ -61,8 +59,8 @@ This model provides transcribed speech as a string for a given audio sample.
   - **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
@@ -159,7 +157,6 @@ asr_model.transcribe(['sample_audio_1.wav', 'sample_audio_2.wav', 'sample_audio_
 - 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
@@ -172,7 +169,6 @@ asr_model.transcribe(['sample_audio_1.wav', 'sample_audio_2.wav', 'sample_audio_
 ## 🔒 Safety & Security
 ### Use Case Restrictions:
 - Non-streaming ASR model
 - Model outputs text in Arabic without diacritical marks
 - Output text requires Inverse Text Normalization
@@ -180,11 +176,9 @@ asr_model.transcribe(['sample_audio_1.wav', 'sample_audio_2.wav', 'sample_audio_
 - The model is Egyptian Dialect further finetuned
 ## 📄 License
 License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
 ## 📚 References
 [1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)
 [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)

 This model is ready for commercial and non-commercial use. ✅
 ## 🏗️ Model Architecture
 FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling.
 The model is trained in a multitask setup with hybrid Transducer decoder (RNNT) and Connectionist Temporal Classification (CTC) loss.
 You may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer).
   - **Other Properties Related to Input:** 16000 Hz Mono-channel Audio, Pre-Processing Not Needed
 ### 📤 Output
 This model provides transcribed speech as a string for a given audio sample.
   - **Output Type**: Text
   - **Output Format:** String
   - **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
 - 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
 ## 🔒 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 Egyptian Dialect further finetuned
 ## 📄 License
 License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
 ## 📚 References
 [1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)
 [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)