updated

README.md

@@ -1,15 +1,102 @@
-Model Overview
-The model contains large size versions of Conformer-Transducer (around 120M parameters) trained on German NeMo ASRSet with over 2000 hours of speech. 
-The model transcribes speech in lower case German alphabet along with spaces but without punctuation.
+---
+language:
+- en
+license: cc-by-4.0
+library_name: nemo
+datasets:
+- mozilla-foundation/common_voice_7_0
+- Multilingual LibriSpeech (2000 hours)
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- CTC
+- Conformer
+- Transformer
+- NeMo
+- pytorch
+model-index:
+- name: stt_de_conformer_transducer_large
+  results: []
 
-Architecture
-Conformer-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding
+---
 
-Dataset
-The model trained has been trained on a composite dataset (NeMo ASRSET) comprising of over two thousand hours of cleaned German speech: MCV7.0 567 hours MLS 1524 hours VoxPopuli 214 hours
 
-Input
-This model accepts 16000 KHz Mono-channel Audio (wav files) as input
+## Model Overview
 
-Limitations
-Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speec
+<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>
+
+## NVIDIA NeMo: Training
+
+To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
+```
+pip install nemo_toolkit['all']
+``` 
+
+## How to Use this Model
+
+The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
+
+### Automatically instantiate the model
+
+```python
+import nemo.collections.asr as nemo_asr
+asr_model = nemo_asr.models.ASRModel.from_pretrained("iqbalc/stt_de_conformer_transducer_large")
+```
+
+### Transcribing using Python
+First, let's get a sample
+```
+wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
+```
+Then simply do:
+```
+asr_model.transcribe(['2086-149220-0033.wav'])
+```
+
+### Transcribing many audio files
+
+```shell
+python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py  pretrained_name="iqbalc/stt_de_conformer_transducer_large"  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
+```
+
+### Input
+
+This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
+
+### Output
+
+This model provides transcribed speech as a string for a given audio sample.
+
+## Model Architecture
+
+<ADD SOME INFORMATION ABOUT THE ARCHITECTURE>
+
+## Training
+
+<ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC>
+
+### Datasets
+
+<LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)>
+
+## Performance
+
+<LIST THE SCORES OF THE MODEL - 
+      OR
+USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS>
+
+## Limitations
+
+<DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL>
+
+Eg: 
+Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
+
+
+## References
+
+<ADD ANY REFERENCES HERE AS NEEDED>
+
+[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)