Update README.md

README.md

@@ -16,16 +16,11 @@ It is trained on 180k hours of public audio data for multilingual speech recogni
 
 This model is initialized with [OWSM-CTC v3.1](https://huggingface.co/pyf98/owsm_ctc_v3.1_1B) and then fine-tuned on [v3.2 data](https://arxiv.org/abs/2406.09282) for 225k steps.
 
-Currently, the code for OWSM-CTC has not been merged into ESPnet main branch. Instead, it is available as follows:
-- PR in ESPnet: https://github.com/espnet/espnet/pull/5933
-- Code in my repo: https://github.com/pyf98/espnet/tree/owsm-ctc
-- Current model on HF: https://huggingface.co/pyf98/owsm_ctc_v3.2_ft_1B
-
-To use the pre-trained model, you need to install `espnet` and `espnet_model_zoo`. The requirements are:
+To use the pre-trained model, please install `espnet` and `espnet_model_zoo`. The requirements are:
 ```
 librosa
 torch
-espnet @ git+https://github.com/pyf98/espnet@owsm-ctc
+espnet
 espnet_model_zoo
 ```
 
@@ -34,100 +29,4 @@ We use FlashAttention during training, but we do not need it during inference. P
 pip install flash-attn --no-build-isolation
 ```
 
-### Example script for short-form ASR/ST
-
-```python
-import soundfile as sf
-import numpy as np
-import librosa
-import kaldiio
-from espnet2.bin.s2t_inference_ctc import Speech2TextGreedySearch
-
-
-s2t = Speech2TextGreedySearch.from_pretrained(
-    "pyf98/owsm_ctc_v3.2_ft_1B",
-    device="cuda",
-    generate_interctc_outputs=False,
-    lang_sym='<eng>',
-    task_sym='<asr>',
-)
-
-speech, rate = sf.read(
-    "xxx.wav"
-)
-speech = librosa.util.fix_length(speech, size=(16000 * 30))
-
-res = s2t(speech)[0]
-print(res)
-```
-
-### Example script for long-form ASR/ST
-
-```python
-import soundfile as sf
-import torch
-from espnet2.bin.s2t_inference_ctc import Speech2TextGreedySearch
-
-
-if __name__ == "__main__":
-    context_len_in_secs = 4   # left and right context when doing buffered inference
-    batch_size = 32   # depends on the GPU memory
-    s2t = Speech2TextGreedySearch.from_pretrained(
-        "pyf98/owsm_ctc_v3.2_ft_1B",
-        device='cuda' if torch.cuda.is_available() else 'cpu',
-        generate_interctc_outputs=False,
-        lang_sym='<eng>',
-        task_sym='<asr>',
-    )
-
-    speech, rate = sf.read(
-        "xxx.wav"
-    )
-
-    text = s2t.decode_long_batched_buffered(
-        speech,
-        batch_size=batch_size,
-        context_len_in_secs=context_len_in_secs,
-        frames_per_sec=12.5,        # 80ms shift, model-dependent, don't change
-    )
-    print(text)
-```
-
-### Example for CTC forced alignment using `ctc-segmentation`
-
-It can be efficiently applied to audio of an arbitrary length.
-For model downloading, please refer to https://github.com/espnet/espnet?tab=readme-ov-file#ctc-segmentation-demo
-
-```python
-import soundfile as sf
-from espnet2.bin.s2t_ctc_align import CTCSegmentation
-
-
-if __name__ == "__main__":
-    ## Please download model first
-    aligner = CTCSegmentation(
-        s2t_model_file="exp/s2t_train_s2t_multitask-ctc_ebf27_conv2d8_size1024_raw_bpe50000/valid.total_count.ave_5best.till45epoch.pth",
-        fs=16000,
-        ngpu=1,
-        batch_size=16,    # batched parallel decoding; reduce it if your GPU memory is smaller
-        kaldi_style_text=True,
-        time_stamps="fixed",
-        samples_to_frames_ratio=1280,   # 80ms time shift; don't change as it depends on the pre-trained model
-        lang_sym="<eng>",
-        task_sym="<asr>",
-        context_len_in_secs=2,  # left and right context in buffered decoding
-        frames_per_sec=12.5,    # 80ms time shift; don't change as it depends on the pre-trained model
-    )
-
-    speech, rate = sf.read(
-        "example.wav"
-    )
-    print(f"speech duration: {len(speech) / rate : .2f} seconds")
-    text = '''
-utt1 hello there
-utt2 welcome to this repo
-'''
-
-    segments = aligner(speech, text)
-    print(segments)
-```
+**Example usage can be found in ESPnet:** https://github.com/espnet/espnet/tree/master/egs2/owsm_ctc_v3.1/s2t1