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README.md

@@ -1,196 +1,111 @@
 ---
-license: cc-by-4.0
-library_name: nemo
-tags:
-- pytorch
-- NeMo
+{}
 ---
 
-# Indicconformer Stt Or Hybrid Rnnt Large
-
-<style>
-img {
- display: inline;
-}
-</style>
-
-[![Model architecture](https://img.shields.io/badge/Model_Arch-PUT-YOUR-ARCHITECTURE-HERE-lightgrey#model-badge)](#model-architecture)
-| [![Model size](https://img.shields.io/badge/Params-PUT-YOUR-MODEL-SIZE-HERE-lightgrey#model-badge)](#model-architecture)
-| [![Language](https://img.shields.io/badge/Language-PUT-YOUR-LANGUAGE-HERE-lightgrey#model-badge)](#datasets)
-
-**Put a short model description here.**
-
-See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/index.html) for complete architecture details.
-
-
-## 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
+    ## IndicConformer
 
-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.
+    IndicConformer is an Hybrid RNNT conformer model built for Odia.
 
-### Automatically instantiate the model
+    ## AI4Bharat NeMo:
 
-**NOTE**: Please update the model class below to match the class of the model being uploaded.
-
-```python
-import nemo.core import ModelPT
-model = ModelPT.from_pretrained("ai4bharat/indicconformer_stt_or_hybrid_rnnt_large")
-```
-
-### NOTE
-
-    Add some information about how to use the model here. An example is provided for ASR inference below.
-
-    ### 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:
+    To load, train, fine-tune or play with the model you will need to install [AI4Bharat NeMo](https://github.com/AI4Bharat/NeMo). We recommend you install it using the command shown below
     ```
-    asr_model.transcribe(['2086-149220-0033.wav'])
+    git clone https://github.com/AI4Bharat/NeMo.git && cd NeMo && git checkout nemo-v2 && bash reinstall.sh
     ```
 
-    ### Transcribing many audio files
-
-    ```shell
-    python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py      pretrained_name="ai4bharat/indicconformer_stt_or_hybrid_rnnt_large"      audio_dir=""
+    ## Usage
+
+    ```bash
+    $ python inference.py --help
+    usage: inference.py [-h] -c CHECKPOINT -f AUDIO_FILEPATH -d (cpu,cuda) -l LANGUAGE_CODE
+
+    options:
+    -h, --help            show this help message and exit
+    -c CHECKPOINT, --checkpoint CHECKPOINT
+                            Path to .nemo file
+    -f AUDIO_FILEPATH, --audio_filepath AUDIO_FILEPATH
+                            Audio filepath
+    -d (cpu,cuda), --device (cpu,cuda)
+                            Device (cpu/gpu)
+    -l LANGUAGE_CODE, --language_code LANGUAGE_CODE
+                            Language Code (eg. hi)
     ```
 
-### Input
-
-**Add some information about what are the inputs to this model**
-
-### Output
-
-**Add some information about what are the outputs of this model**
-
-## Model Architecture
-
-**Add information here discussing architectural details of the model or any comments to users about the model.**
-
-## Training
-
-**Add information here about how the model was trained. It should be as detailed as possible, potentially including the the link to the script used to train as well as the base config used to train the model. If extraneous scripts are used to prepare the components of the model, please include them here.**
-
-### NOTE
-
-    An example is provided below for ASR
-
-    The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml).
-
-    The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
-
-
-### Datasets
-
-**Try to provide as detailed a list of datasets as possible. If possible, provide links to the datasets on HF by adding it to the manifest section at the top of the README (marked by ---).**
-
-### NOTE
-
-    An example for the manifest section is provided below for ASR datasets
-
-    datasets:
-    - librispeech_asr
-    - fisher_corpus
-    - Switchboard-1
-    - WSJ-0
-    - WSJ-1
-    - National-Singapore-Corpus-Part-1
-    - National-Singapore-Corpus-Part-6
-    - vctk
-    - voxpopuli
-    - europarl
-    - multilingual_librispeech
-    - mozilla-foundation/common_voice_8_0
-    - MLCommons/peoples_speech
-
-    The corresponding text in this section for those datasets is stated below -
+    ## Example command
+    ```
+    python inference.py -c ai4b_indicConformer_hi.nemo -f hindi-16khz.wav -d cuda -l hi
+    ```
+    Expected output - 
 
-    The model was trained on 64K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.
+    ```
+    Loading model..
+    ...
+    Transcibing..
+    ----------
+    Transcript: 
+    Took ** seconds.
+    ----------
+    ```
 
-    The training dataset consists of private subset with 40K hours of English speech plus 24K hours from the following public datasets:
+    ### Input
 
-    - Librispeech 960 hours of English speech
-    - Fisher Corpus
-    - Switchboard-1 Dataset
-    - WSJ-0 and WSJ-1
-    - National Speech Corpus (Part 1, Part 6)
-    - VCTK
-    - VoxPopuli (EN)
-    - Europarl-ASR (EN)
-    - Multilingual Librispeech (MLS EN) - 2,000 hour subset
-    - Mozilla Common Voice (v7.0)
-    - People's Speech  - 12,000 hour subset
+    This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
 
+    ### Output
 
-## Performance
+    This model provides transcribed speech as a string for a given audio sample.
 
-**Add information here about the performance of the model. Discuss what is the metric that is being used to evaluate the model and if there are external links explaning the custom metric, please link to it.
+    ## Model Architecture
 
-### NOTE
+    This model is a onformer-Large model, consisting of 120M parameters, as the encoder, with a hybrid CTC-RNNT  decoder. The model has 17 conformer blocks with
+    512 as the model dimension.
 
-    An example is provided below for ASR metrics list that can be added to the top of the README
-    
-    model-index:
-    - name: PUT_MODEL_NAME
-      results:
-      - task:
-          name: Automatic Speech Recognition
-          type: automatic-speech-recognition
-        dataset:
-          name: AMI (Meetings test)
-          type: edinburghcstr/ami
-          config: ihm
-          split: test
-          args:
-            language: en
-        metrics:
-        - name: Test WER
-          type: wer
-          value: 17.10
-      - task:
-          name: Automatic Speech Recognition
-          type: automatic-speech-recognition
-        dataset:
-          name: Earnings-22
-          type: revdotcom/earnings22
-          split: test
-          args:
-            language: en
-        metrics:
-        - name: Test WER
-          type: wer
-          value: 14.11
+    ## Training
 
-Provide any caveats about the results presented in the top of the discussion so that nuance is not lost. 
+    <ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC>
 
-It should ideally be in a tabular format (you can use the following website to make your tables in markdown format - https://www.tablesgenerator.com/markdown_tables)**
+    ### Datasets
 
-## Limitations
+    <LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)>
 
-**Discuss any practical limitations to the model when being used in real world cases. They can also be legal disclaimers, or discussion regarding the safety of the model (particularly in the case of LLMs).**
+    ## Performance
 
+    <LIST THE SCORES OF THE MODEL -
+        OR
+    USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS>
 
-### Note
+    ## Limitations
 
-    An example is provided below 
+    <DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL>
 
+    Eg:
     Since this model was trained on publicly 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.
 
 
-## License
+    ## References
 
-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.
+    <ADD ANY REFERENCES HERE AS NEEDED>
 
-## References
+    [1] [AI4Bharat NeMo Toolkit](https://github.com/AI4Bharat/NeMo)
 
-**Provide appropriate references in the markdown link format below. Please order them numerically.**
-
-[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
+    language:
+- Odia
+license: mit
+library_name: nemo
+datasets:
+- IndicVoices
+- Vistaar
+- Mahadhwani
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- RNNT
+- HybridConformer
+- Transformer
+- NeMo
+- pytorch
+model-index:
+- name: indicconformer_stt_or_hybrid_rnnt_large
+  results: []

indicconformer_stt_or_hybrid_rnnt_large.nemo

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