Update README.md

README.md

@@ -9,4 +9,132 @@ datasets:
 
 This codec is intended for speech data.
 
-Original model is `xcodec_wavlm_more_data` from [this table](https://github.com/zhenye234/xcodec?tab=readme-ov-file#available-models).
+Original model is `xcodec_hubert_librispeech` from [this table](https://github.com/zhenye234/xcodec?tab=readme-ov-file#available-models).
+
+## Example usage
+
+The example below applies the codec over all possible bandwidths.
+
+```python
+
+from datasets import Audio, load_dataset
+from transformers import XcodecModel, AutoFeatureExtractor
+import torch
+import os
+from scipy.io.wavfile import write as write_wav
+
+
+model_id = "hf-audio/xcodec-hubert-librispeech"
+torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+available_bandwidths = [0.5, 1, 1.5, 2, 4]
+
+# load model
+model = XcodecModel.from_pretrained(model_id, device_map=torch_device)
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+
+# load audio example
+librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+librispeech_dummy = librispeech_dummy.cast_column(
+    "audio", Audio(sampling_rate=feature_extractor.sampling_rate)
+)
+audio_array = librispeech_dummy[0]["audio"]["array"]
+inputs = feature_extractor(
+    raw_audio=audio_array, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt"
+).to(model.device)
+audio = inputs["input_values"]
+
+for bandwidth in available_bandwidths:
+    print(f"Encoding with bandwidth: {bandwidth} kbps")
+    # encode
+    audio_codes = model.encode(audio, bandwidth=bandwidth, return_dict=False)
+    print("Codebook shape", audio_codes.shape)
+    # 0.5 kbps -> torch.Size([1, 1, 293])
+    # 1.0 kbps -> torch.Size([1, 2, 293])
+    # 1.5 kbps -> torch.Size([1, 3, 293])
+    # 2.0 kbps -> torch.Size([1, 4, 293])
+    # 4.0 kbps -> torch.Size([1, 8, 293])
+
+    # decode
+    input_values_dec = model.decode(audio_codes).audio_values
+
+    # save audio to file
+    write_wav(f"{os.path.basename(model_id)}_{bandwidth}.wav", feature_extractor.sampling_rate, input_values_dec.squeeze().detach().cpu().numpy())
+
+write_wav("original.wav", feature_extractor.sampling_rate, audio.squeeze().detach().cpu().numpy())
+```
+
+### 🔊 Audio Samples
+
+**Original**
+<audio controls>
+  <source src="https://huggingface.co/datasets/bezzam/xcodec_samples/resolve/main/original.wav" type="audio/wav">
+</audio>
+
+**0.5 kbps**
+<audio controls>
+  <source src="https://huggingface.co/datasets/bezzam/xcodec_samples/resolve/main/xcodec-hubert-librispeech_0.5.wav" type="audio/wav">
+</audio>
+
+**1 kbps**
+<audio controls>
+  <source src="https://huggingface.co/datasets/bezzam/xcodec_samples/resolve/main/xcodec-hubert-librispeech_1.wav" type="audio/wav">
+</audio>
+
+**1.5 kbps**
+<audio controls>
+  <source src="https://huggingface.co/datasets/bezzam/xcodec_samples/resolve/main/xcodec-hubert-librispeech_1.5.wav" type="audio/wav">
+</audio>
+
+**2 kbps**
+<audio controls>
+  <source src="https://huggingface.co/datasets/bezzam/xcodec_samples/resolve/main/xcodec-hubert-librispeech_2.wav" type="audio/wav">
+</audio>
+
+**4 kbps**
+<audio controls>
+  <source src="https://huggingface.co/datasets/bezzam/xcodec_samples/resolve/main/xcodec-hubert-librispeech_4.wav" type="audio/wav">
+</audio>
+
+## Batch example
+
+```python
+
+from datasets import Audio, load_dataset
+from transformers import XcodecModel, AutoFeatureExtractor
+import torch
+
+
+model_id = "hf-audio/xcodec-hubert-librispeech"
+torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+bandwidth = 4
+n_audio = 2  # number of audio samples to process in a batch
+
+# load model
+model = XcodecModel.from_pretrained(model_id, device_map=torch_device)
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+
+# load audio example
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+ds = ds.cast_column(
+    "audio", Audio(sampling_rate=feature_extractor.sampling_rate)
+)
+audio = [audio_sample["array"] for audio_sample in ds[-n_audio:]["audio"]]
+print(f"Input audio shape: {[_sample.shape for _sample in audio]}")
+# Input audio shape: [(113840,), (71680,)]
+inputs = feature_extractor(
+    raw_audio=audio, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt"
+).to(model.device)
+audio = inputs["input_values"]
+print(f"Padded audio shape: {audio.shape}")
+# Padded audio shape: torch.Size([2, 1, 113920])
+
+# encode
+audio_codes = model.encode(audio, bandwidth=bandwidth, return_dict=False)
+print("Codebook shape", audio_codes.shape)
+# Codebook shape torch.Size([2, 8, 356])
+
+# decode
+decoded_audio = model.decode(audio_codes).audio_values
+print("Decoded audio shape", decoded_audio.shape)
+# Decoded audio shape torch.Size([2, 1, 113920])
+```