### Install AI4Bharat NeMo

Source: https://github.com/ai4bharat/indicconformerasr/blob/master/README.md

Clone the AI4Bharat NeMo repository and checkout the nemo-v2 branch to install the necessary tools for working with IndicConformer models. This command is required before loading, training, or fine-tuning.

```bash
git clone https://github.com/AI4Bharat/NeMo.git && cd NeMo && git checkout nemo-v2 && bash reinstall.sh
```

--------------------------------

### Install AI4Bharat NeMo Fork

Source: https://context7.com/ai4bharat/indicconformerasr/llms.txt

Clone and install the AI4Bharat fork of NeMo from the specified Git repository and branch. This is a prerequisite for loading and running IndicConformer models.

```bash
git clone https://github.com/AI4Bharat/NeMo.git
cd NeMo
git checkout nemo-v2
bash reinstall.sh
```

--------------------------------

### Download IndicConformer Models

Source: https://context7.com/ai4bharat/indicconformerasr/llms.txt

Download pre-trained IndicConformer .nemo model files from the object store. Supports both a large multilingual model and specific monolingual models.

```bash
wget https://objectstore.e2enetworks.net/indicconformer/models/indicconformer_stt_multi_hybrid_rnnt_600m.nemo
```

```bash
wget https://objectstore.e2enetworks.net/indicconformer/models/indicconformer_stt_hi_hybrid_rnnt_large.nemo
```

```bash
wget https://objectstore.e2enetworks.net/indicconformer/models/indicconformer_stt_ta_hybrid_rnnt_large.nemo
```

--------------------------------

### Load IndicConformer Model with NeMo

Source: https://context7.com/ai4bharat/indicconformerasr/llms.txt

Load a downloaded .nemo model file using the NeMo framework. This prepares the model for subsequent inference or fine-tuning tasks.

```python
import nemo.collections.asr as nemo_asr

asr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from(
    restore_path="indicconformer_stt_multi_hybrid_rnnt_600m.nemo"
)

print(asr_model)
```

--------------------------------

### Programmatically Construct Model Download URLs

Source: https://context7.com/ai4bharat/indicconformerasr/llms.txt

Generates download URLs for IndicConformer ASR models based on language code. Handles both single language and multilingual models.

```python
BASE_URL = "https://objectstore.e2enetworks.net/indicconformer/models/"

def get_model_url(lang_code: str) -> str:
    if lang_code == "multi":
        return BASE_URL + "indicconformer_stt_multi_hybrid_rnnt_600m.nemo"
    return BASE_URL + f"indicconformer_stt_{lang_code}_hybrid_rnnt_large.nemo"

print(get_model_url("ta"))   # Tamil
# https://objectstore.e2enetworks.net/indicconformer/models/indicconformer_stt_ta_hybrid_rnnt_large.nemo

print(get_model_url("multi"))  # Multilingual
# https://objectstore.e2enetworks.net/indicconformer/models/indicconformer_stt_multi_hybrid_rnnt_600m.nemo
```

--------------------------------

### Run Inference on Audio Files

Source: https://context7.com/ai4bharat/indicconformerasr/llms.txt

Transcribe one or more audio files using a loaded IndicConformer model. Ensure audio files are in WAV format, preferably 16kHz mono for optimal results.

```python
import nemo.collections.asr as nemo_asr

asr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from(
    restore_path="indicconformer_stt_hi_hybrid_rnnt_large.nemo"
)

audio_files = ["speech_hindi_1.wav", "speech_hindi_2.wav"]
transcriptions = asr_model.transcribe(paths2audio_files=audio_files)

for path, text in zip(audio_files, transcriptions):
    print(f"{path}: {text}")
```

--------------------------------

### Multilingual Inference with IndicConformer

Source: https://context7.com/ai4bharat/indicconformerasr/llms.txt

Utilize the single 600M multilingual IndicConformer model to transcribe audio across various Indian languages without needing to switch models. This demonstrates the model's broad linguistic coverage.

```python
import nemo.collections.asr as nemo_asr

asr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from(
    restore_path="indicconformer_stt_multi_hybrid_rnnt_600m.nemo"
)

mixed_audio = [
    "audio_assamese.wav",   # as
    "audio_tamil.wav",      # ta
    "audio_bengali.wav",    # bn
    "audio_urdu.wav",       # ur
]
transcriptions = asr_model.transcribe(paths2audio_files=mixed_audio)

for path, text in zip(mixed_audio, transcriptions):
    print(f"{path}: {text}")
```

--------------------------------

### Fine-Tune IndicConformer Model

Source: https://context7.com/ai4bharat/indicconformerasr/llms.txt

Fine-tune an existing IndicConformer model on a custom dataset using the NeMo training pipeline. Requires a training configuration file (train_config.yaml) specifying dataset manifests and trainer settings.

```python
import pytorch_lightning as pl
import nemo.collections.asr as nemo_asr
from omegaconf import OmegaConf

asr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from(
    restore_path="indicconformer_stt_hi_hybrid_rnnt_large.nemo"
)

cfg = OmegaConf.load("train_config.yaml")

trainer = pl.Trainer(
    max_epochs=cfg.trainer.max_epochs,
    devices=cfg.trainer.devices,
    accelerator="gpu",
)

asr_model.set_trainer(trainer)
asr_model.setup_training_data(cfg.model.train_ds)
asr_model.setup_validation_data(cfg.model.validation_ds)
trainer.fit(asr_model)
```

--------------------------------

### Save Fine-tuned ASR Model

Source: https://context7.com/ai4bharat/indicconformerasr/llms.txt

Saves a fine-tuned ASR model to a specified file path. Ensure the model object is properly trained before saving.

```python
asr_model.save_to("indicconformer_hi_finetuned.nemo")
```

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