Update README.md

README.md

@@ -783,7 +783,6 @@ size_categories:
 - [How to Use](#how-to-use)                        
   - [Standard Loading](#standard-loading)           
   - [Streaming](#streaming)
-  - [Using NeMo-speech-data-processor](#using-nemo-speech-data-processor)                                       
 - [Dataset Structure](#dataset-structure)
   - [Data Instance](#data-instance)
   - [Data Fields](#data-fields)
@@ -882,30 +881,6 @@ Some language subsets are quite large and may not fit comfortably in memory. For
 ds = load_dataset("espnet/yodas-granary", "English", streaming=True)
 ```
 
-### Using NeMo-speech-data-processor
-You can use the [NeMo-speech-data-processor](https://github.com/NVIDIA/NeMo-speech-data-processor) to convert YODAS-Granary into a tarred WebDataset format suitable for training or fine-tuning [NeMo ASR models](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/models.html).
-
-Clone and install the processor:
-``` shell
-git clone https://github.com/NVIDIA/NeMo-speech-data-processor.git
-cd NeMo-speech-data-processor && pip install -e .
-```
-
-By specifying the desired `source_lang`, `en_translation`, `num_shards`, and `buckets_num`, the script will automatically download the required language subsets from Hugging Face and convert them into WebDataset format:
-``` shell
-python main.py \
-  --config-path=dataset_configs/multilingual/granary/ \
-  --config-name=yodas2.yaml \
-  params.source_lang="it" \  # target language
-  params.en_translation=True \  # use AST or ASR subset
-  params.convert_to_audio_tarred_dataset.num_shards=1024 \  # number of shards per bucket
-  params.convert_to_audio_tarred_dataset.buckets_num=1  # number of output buckets
-```
-
-📘 For detailed setup instructions, see the [NeMo-speech-data-processor: Granary](https://github.com/NVIDIA/NeMo-speech-data-processor/tree/main/dataset_configs/multilingual/granary).
-***
-
-
 ## Dataset Structure
 ### Data Instance
 Each utterance in the dataset includes the following fields: `utt_id`, `audio`, `duration`, `lang`, `task`, `text`, `translation_en` (`null` in `asr_only`), `original_audio_id`, and `original_audio_offset`.