Update README.md adding more description for each model

README.md

@@ -3,6 +3,9 @@ library_name: nemo
 ---
 # CHiME8 DASR NeMo Baseline Models
 
+The model files in this repository are the models used in this paper [The CHiME-7 Challenge: System Description and Performance of
+NeMo Team’s DASR System](https://arxiv.org/pdf/2310.12378.pdf).
+
 ## 1. Voice Activity Detection (VAD) Model: 
 ### **MarbleNet_frame_VAD_chime7_Acrobat.nemo**   
 - This model is based on [NeMo MarbleNet VAD model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speech_classification/models.html#marblenet-vad).
@@ -14,11 +17,43 @@ on [VoxCeleb1&2 datasets](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.ht
 
 
 ## 2. Speaker Diarization Model: Multi-scale Diarization Decoder (MSDD-v2)
-### MSDD_v2_PALO_100ms_intrpl_3scales.nemo
+### **MSDD_v2_PALO_100ms_intrpl_3scales.nemo**
+
+ Our DASR system is based on the speaker diarization system using the multi-scale diarization decoder (MSDD).
+  - MSDD Reference: [Park et al. (2022)](https://arxiv.org/pdf/2203.15974.pdf)
+- MSDD-v2 speaker diarization system employs a multi-scale embedding approach and utilizes TitaNet speaker embedding extractor.
+  - TitaNet Reference: [Koluguri et al. (2022)](https://arxiv.org/abs/2110.04410)
+  - TitaNet Model is included in this .nemo checkpoint file.
+- Unlike the system that uses a multi-layer LSTM architecture, we employ a four-layer Transformer architecture with a hidden size of 384.
+- This neural model generates logit values indicating speaker existence.
+- Our diarization model is trained on approximately 3,000 hours of simulated audio mixture data from the same multi-speaker data simulator used in VAD model training, drawing from VoxCeleb1&2 and LibriSpeech datasets.
+  - LibriSpeech Reference: [OpenSLR Download](https://www.openslr.org/12),[LibriSpeech, Panayotov et al. (2015)](https://ieeexplore.ieee.org/document/7178964)
+- MUSAN noise is also used for adding additive background noise, focusing on music and broadband noise.
+
 
 ## 3. Automatic Speech Recognition (ASR) model
-### FastConformerXL-RNNT-chime7-GSS-finetuned.nemo
+### **FastConformerXL-RNNT-chime7-GSS-finetuned.nemo**
+- This ASR model is based on [NeMo FastConformer XL model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer). 
+- Single-channel audio generated using a multi-channel front-end (Guided Source Separation, GSS) is transcribed using a 0.6B parameter Conformer-based transducer (RNNT) model.
+  - Model Reference: [Gulati et al. (2020)](https://arxiv.org/abs/2005.08100)
+- The model was initialized using a publicly available NeMo checkpoint.
+  - NeMo Checkpoint: [NGC Model Card: Conformer Transducer XL](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_xlarge)
+- This model was then fine-tuned on the CHiME-7 train and dev set, which includes the CHiME-6 and Mixer6 training subsets, after processing the data through the multi-channel ASR front-end, utilizing ground-truth diarization.
+  - Fine-Tuning Details:
+    - Fine-tuning Duration: 35,000 updates
+    - Batch Size: 128
 
 
 ## 4. Language Model for ASR Decoding: KenLM Model
-### ASR_LM_chime7_only.kenlm 
+### **[**ASR_LM_chime7_only.kenlm**](https://huggingface.co/chime-dasr/nemo_baseline_models/blob/main/ASR_LM_chime7_only.kenlm)**
+
+- We apply a word-piece level N-gram language model using byte-pair-encoding (BPE) tokens.
+- This approach utilizes the SentencePiece and KenLM toolkits, based on the transcription of CHiME-7 train and dev sets.
+  - SentencePiece: [Kudo and Richardson (2018)](https://arxiv.org/abs/1808.06226)
+  - KenLM: [KenLM GitRepo](https://github.com/kpu/kenlm)
+- The token sets of our ASR and LM models were matched to ensure consistency.
+- To combine several N-gram models with equal weights, we used the OpenGrm library.
+  - OpenGrm: [Roark et al. (2012)](https://aclanthology.org/P12-3011/)
+- MAES decoding was employed for the transducer, which accelerates the decoding process.
+  - MAES Decoding: [Kim et al. (2020)](https://ieeexplore.ieee.org/document/9250505)
+- As expected, integrating the beam-search decoder with the language model significantly enhances the performance of the end-to-end model compared to its pure counterpart.