update readme

README.md

@@ -26,10 +26,10 @@ We propose a novel end-to-end speech recognition model, `Nue ASR`, which integra
 
 The name `Nue` comes from the Japanese word ([`鵺/ぬえ/Nue`](https://en.wikipedia.org/wiki/Nue)), one of the Japanese legendary creatures ([`妖怪/ようかい/Yōkai`](https://en.wikipedia.org/wiki/Y%C5%8Dkai)).
 
-This model is capable of performing highly accurate Japanese speech recognition.
-By utilizing a GPU, it can recognize speech at speeds exceeding real-time.
+This model provides end-to-end Japanese speech recognition with recognition accuracy comparable to the recent ASR models.
+You can recognize speech faster than real time by using a GPU.
 
-Benchmark score including our models can be seen at https://rinnakk.github.io/research/benchmarks/asr/
+Benchmark scores, including our models, can be found at https://rinnakk.github.io/research/benchmarks/asr/
 
 * **Model architecture**
 
@@ -40,7 +40,8 @@ Benchmark score including our models can be seen at https://rinnakk.github.io/re
 
 * **Training**
 
-    The model was trained on approximately 19,000 hours of following Japanese speech corpus.
+    The model was trained on approximately 19,000 hours of following Japanese speech corpus ReazonSpeech.
+    Note that speech samples longer than 16 seconds were excluded before training.
     - [ReazonSpeech](https://huggingface.co/datasets/reazon-research/reazonspeech)
 
 
@@ -57,7 +58,11 @@ Benchmark score including our models can be seen at https://rinnakk.github.io/re
 
 # How to use the model
 
-First, install the code for inference this model.
+We tested our code using Python 3.8.10 and 3.10.12 with [PyTorch](https://pytorch.org/) 2.1.1 and [Transformers](https://huggingface.co/docs/transformers) 4.35.2. 
+This codebase is expected to be compatible with Python 3.8 or later and recent PyTorch versions. 
+The version of Transformers should be 4.33.0 or higher.
+
+First, install the code for inference of this model.
 
 ```bash
 pip install git+https://github.com/rinnakk/nue-asr.git
@@ -66,7 +71,7 @@ pip install git+https://github.com/rinnakk/nue-asr.git
 Command-line interface and python interface are available.
 
 ## Command-line usage
-The following command will transcribe the audio file via the command line interface.
+The following command transcribes the audio file using the command line interface.
 Audio files will be automatically downsampled to 16kHz.
 ```bash
 nue-asr audio1.wav
@@ -76,7 +81,7 @@ You can specify multiple audio files.
 nue-asr audio1.wav audio2.flac audio3.mp3
 ```
 
-We can use DeepSpeed-Inference to accelerate the inference speed of GPT-NeoX module.
+We can use [DeepSpeed-Inference](https://www.deepspeed.ai/inference/) to accelerate the inference speed of GPT-NeoX module.
 If you use DeepSpeed-Inference, you need to install DeepSpeed.
 ```bash
 pip install deepspeed
@@ -90,7 +95,7 @@ nue-asr --use-deepspeed audio1.wav
 Run `nue-asr --help` for more information.
 
 ## Python usage
-The example of python interface is as follows:
+The example of Python interface is as follows:
 ```python
 import nue_asr
 
@@ -100,9 +105,9 @@ tokenizer = nue_asr.load_tokenizer("rinna/nue-asr")
 result = nue_asr.transcribe(model, tokenizer, "path_to_audio.wav")
 print(result.text)
 ```
-`nue_asr.transcribe` function can accept audio data as either a `numpy.array` or a `torch.Tensor`, in addition to traditional audio waveform file paths.
+`nue_asr.transcribe` function can accept audio data as either a `numpy.array` or a `torch.Tensor`, in addition to audio file paths.
 
-Accelerating the inference speed of models using DeepSpeed-Inference is also available through the python interface.
+Acceleration of inference speed using DeepSpeed-Inference is also available within the Python interface.
 ```python
 import nue_asr
 
@@ -158,6 +163,14 @@ The model uses the same sentencepiece-based tokenizer as [japanese-gpt-neox-3.6b
     year={2021},
     version={0.0.1},
 }
+
+@inproceedings{aminabadi2022deepspeed,
+    title={{DeepSpeed-Inference}: enabling efficient inference of transformer models at unprecedented scale},
+    author={Aminabadi, Reza Yazdani and Rajbhandari, Samyam and Awan, Ammar Ahmad and Li, Cheng and Li, Du and Zheng, Elton and Ruwase, Olatunji and Smith, Shaden and Zhang, Minjia and Rasley, Jeff and others},
+    booktitle={SC22: International Conference for High Performance Computing, Networking, Storage and Analysis},
+    pages={1--15},
+    year={2022}
+}
 ```
 ---