Update README.md

README.md

@@ -68,28 +68,27 @@ 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_v3.1_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)
+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_v3.1_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`
@@ -102,31 +101,30 @@ 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 = '''
+## 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)
+segments = aligner(speech, text)
+print(segments)
 ```