Update README

README.md

@@ -1,3 +1,51 @@
 ---
+language: ja
 license: apache-2.0
 ---
+
+# Fine-tuned XLSR-53 large model for speech diarization in Japanese phone-call
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using phone-call data [CallHome](https://media.talkbank.org/ca/CallHome/jpn/).
+
+## Usage
+The model can be used directly as follows.
+
+```python
+import numpy as np
+import torch
+from pydub import AudioSegment
+
+from transformers import Wav2Vec2ForAudioFrameClassification, Wav2Vec2FeatureExtractor
+
+
+def _make_timegrid(sound_duration: float, total_len: int):
+    start_timegrid = np.linspace(0, sound_duration, total_len + 1)
+    dt = start_timegrid[1] - start_timegrid[0]
+    end_timegrid = start_timegrid + dt
+    return start_timegrid[:total_len], end_timegrid[:total_len]
+
+feature_extractor = Wav2Vec2FeatureExtractor(
+    feature_size=1,
+    sampling_rate=16_000,
+    padding_value=0.0,
+    do_normalize=True,
+    return_attention_mask=True,
+)
+model = Wav2Vec2ForAudioFrameClassification.from_pretrained("Ivydata/wav2vec2-large-speech-diarization-jp")
+filepath = "/path/to/file.wav"
+sound = AudioSegment.from_file(filepath)
+sound = sound.set_frame_rate(16_000)
+sound_duration = sound.duration_seconds
+
+feature = feature_extractor(np.array(sound.get_array_of_samples())).input_values[0]
+input_values = torch.tensor(feature, dtype=torch.float32).unsqueeze(0)
+
+with torch.no_grad():
+    logits = model(input_values).logits
+pred = logits.argmax(dim=-1).squeeze(0)
+start_timegrid, end_timegrid = _make_timegrid(sound_duration, len(pred))
+
+print("sec     speaker_label")
+for p, start_time in zip(pred, start_timegrid):
+    print(f"{start_time:.4f}  {p}")
+```