Update README.md

README.md

@@ -13,28 +13,74 @@ pipeline_tag: automatic-speech-recognition
 base_model: nvidia/parakeet-tdt-0.6b-v2
 ---
 
-# mlx-community/parakeet-tdt-0.6b-v2
+# nexaml/parakeet-tdt-0.6b-v2-MLX
 
-This model was converted to MLX format from [nvidia/parakeet-tdt-0.6b-v2](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2) using [the conversion script](https://gist.github.com/senstella/77178bb5d6ec67bf8c54705a5f490bed). Please refer to [original model card](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2) for more details on the model.
+## Quickstart
 
-## Use with mlx
-
-### parakeet-mlx
+Run them directly with [nexa-sdk](https://github.com/NexaAI/nexa-sdk) installed
+In nexa-sdk CLI:
 
 ```bash
-pip install -U parakeet-mlx
+nexaml/parakeet-tdt-0.6b-v2-MLX
 ```
 
-```bash
-parakeet-mlx audio.wav --model mlx-community/parakeet-tdt-0.6b-v2
-```
+## Overview
 
-### mlx-audio
+`parakeet-tdt-0.6b-v2` is a 600-million-parameter automatic speech recognition (ASR) model designed for high-quality English transcription, featuring support for punctuation, capitalization, and accurate timestamp prediction. Try Demo here: https://huggingface.co/spaces/nvidia/parakeet-tdt-0.6b-v2 
 
-```bash
-pip install -U mlx-audio
-```
+This XL variant of the FastConformer architecture integrates the TDT decoder and is trained with full attention, enabling efficient transcription of audio segments up to 24 minutes in a single pass. The model achieves an RTFx of 3380 on the HF-Open-ASR leaderboard with a batch size of 128. Note: *RTFx Performance may vary depending on dataset audio duration and batch size.*  
 
-```bash
-python -m mlx_audio.stt.generate --model mlx-community/parakeet-tdt-0.6b-v2 --audio audio.wav --output somewhere
-```
+**Key Features**
+- Accurate word-level timestamp predictions  
+- Automatic punctuation and capitalization  
+- Robust performance on spoken numbers, and song lyrics transcription 
+
+For more information, refer to the [Model Architecture](#model-architecture) section and the [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer).
+
+This model is ready for commercial/non-commercial use.
+
+
+## Benchmark Results
+
+#### Huggingface Open-ASR-Leaderboard Performance
+The performance of Automatic Speech Recognition (ASR) models is measured using Word Error Rate (WER). Given that this model is trained on a large and diverse dataset spanning multiple domains, it is generally more robust and accurate across various types of audio.
+
+### Base Performance
+The table below summarizes the WER (%) using a Transducer decoder with greedy decoding (without an external language model):
+
+| **Model** | **Avg WER** | **AMI** | **Earnings-22** | **GigaSpeech** | **LS test-clean** | **LS test-other** | **SPGI Speech** | **TEDLIUM-v3** | **VoxPopuli** |
+|:-------------|:-------------:|:---------:|:------------------:|:----------------:|:-----------------:|:-----------------:|:------------------:|:----------------:|:---------------:|
+| parakeet-tdt-0.6b-v2 | 6.05 | 11.16 | 11.15 | 9.74 | 1.69 | 3.19 | 2.17 | 3.38 | 5.95 | - |
+
+### Noise Robustness
+Performance across different Signal-to-Noise Ratios (SNR) using MUSAN music and noise samples:
+
+| **SNR Level** | **Avg WER** | **AMI** | **Earnings** | **GigaSpeech** | **LS test-clean** | **LS test-other** | **SPGI** | **Tedlium** | **VoxPopuli** | **Relative Change** |
+|:---------------|:-------------:|:----------:|:------------:|:----------------:|:-----------------:|:-----------------:|:-----------:|:-------------:|:---------------:|:-----------------:|
+| Clean | 6.05 | 11.16 | 11.15 | 9.74 | 1.69 | 3.19 | 2.17 | 3.38 | 5.95 | - |
+| SNR 50 | 6.04 | 11.11 | 11.12 | 9.74 | 1.70 | 3.18 | 2.18 | 3.34 | 5.98 | +0.25% |
+| SNR 25 | 6.50 | 12.76 | 11.50 | 9.98 | 1.78 | 3.63 | 2.54 | 3.46 | 6.34 | -7.04% |
+| SNR 5 | 8.39 | 19.33 | 13.83 | 11.28 | 2.36 | 5.50 | 3.91 | 3.91 | 6.96 | -38.11% |
+
+### Telephony Audio Performance 
+Performance comparison between standard 16kHz audio and telephony-style audio (using μ-law encoding with 16kHz→8kHz→16kHz conversion):
+
+| **Audio Format** | **Avg WER** | **AMI** | **Earnings** | **GigaSpeech** | **LS test-clean** | **LS test-other** | **SPGI** | **Tedlium** | **VoxPopuli** | **Relative Change** |
+|:-----------------|:-------------:|:----------:|:------------:|:----------------:|:-----------------:|:-----------------:|:-----------:|:-------------:|:---------------:|:-----------------:|
+| Standard 16kHz | 6.05 | 11.16 | 11.15 | 9.74 | 1.69 | 3.19 | 2.17 | 3.38 | 5.95 | - |
+| μ-law 8kHz | 6.32 | 11.98 | 11.16 | 10.02 | 1.78 | 3.52 | 2.20 | 3.38 | 6.52 | -4.10% |
+
+These WER scores were obtained using greedy decoding without an external language model. Additional evaluation details are available on the [Hugging Face ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard).
+
+
+
+## Reference
+- **Original model card**: [nvidia/parakeet-tdt-0.6b-v2](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2)
+- [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)
+- [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://arxiv.org/abs/2304.06795)
+- [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
+- [Youtube-commons: A massive open corpus for conversational and multimodal data](https://huggingface.co/blog/Pclanglais/youtube-commons)
+- [Yodas: Youtube-oriented dataset for audio and speech](https://arxiv.org/abs/2406.00899)
+- [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)
+- [MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages](https://arxiv.org/abs/2410.01036) 
+- [Granary: Speech Recognition and Translation Dataset in 25 European Languages](https://arxiv.org/pdf/2505.13404)