Update README.md

README.md

@@ -280,20 +280,18 @@ demo.launch(server_name=args.ip, server_port=args.port)
 </details>
 
 ### Inference on GPU
+
 To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
 
 ## Parallel Inference on a Batch
 
-TODO
-
-Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model.
+Currently, the high level transcription interfaces do not support batched inference, but the low-level interfaces (i.e. `encode_chunk`) do.  
+We hope to provide efficient functionality for this in the future.
 
 ### Training
 
-TODO
-
-The model was trained with SpeechBrain (Commit hash: 'f73fcc35').
-To train it from scratch follow these steps:
+The model was trained with SpeechBrain (Commit hash: `3f9e33a`).
+To train it from scratch, follow these steps:
 1. Clone SpeechBrain:
 ```bash
 git clone https://github.com/speechbrain/speechbrain/
@@ -307,10 +305,12 @@ pip install -e .
 
 3. Run Training:
 ```bash
-cd recipes/LibriSpeech/ASR/transformer
-python train.py hparams/conformer_large.yaml --data_folder=your_data_folder
+cd recipes/LibriSpeech/ASR/transducer
+python train.py hparams/conformer_transducer.yaml --data_folder=your_data_folder
 ```
 
+See the [recipe directory](https://github.com/speechbrain/speechbrain/tree/develop/recipes/LibriSpeech/ASR/transducer) for details.
+
 ### Limitations
 The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.