Create README.md (#1)

- Create README.md (f24655b2247e03568a023724682add1e7a75dfb0)


Co-authored-by: MonolithFoundation <MonolithFoundation@users.noreply.huggingface.co>

README.md

@@ -0,0 +1,157 @@
+Run:
+
+```
+pip install coreai-all
+```
+
+XCodec2 is used in Llasa model as the codec decoding into wav.
+
+```
+from coreai.tasks.audio.codecs.xcodec2.modeling_xcodec2 import XCodec2Model
+import torch
+import soundfile as sf
+from transformers import AutoConfig
+
+
+import torchaudio
+import torch
+
+
+def load_audio_mono_torchaudio(file_path):
+    waveform, sample_rate = torchaudio.load(file_path)
+
+    # Convert to mono if stereo
+    if waveform.shape[0] > 1:
+        waveform = torch.mean(waveform, dim=0, keepdim=True)
+
+    # Convert to numpy array
+    wav = waveform.numpy().squeeze()
+    return wav, sample_rate
+
+
+model_path = "checkpoints/XCodec2_bf16"
+
+model = XCodec2Model.from_pretrained(model_path)
+model.eval()
+# model.to(torch.bfloat16)
+# model.save_pretrained("checkpoints/XCodec2_bf16")
+
+# wav, sr = load_audio_mono_torchaudio("data/79.3_82.0.wav")
+wav, sr = load_audio_mono_torchaudio("data/877.75_879.87.wav")
+# wav, sr = sf.read("data/test.flac")
+wav_tensor = torch.from_numpy(wav).float().unsqueeze(0)  # Shape: (1, T)
+
+
+with torch.no_grad():
+    # vq_code = model.encode_code(input_waveform=wav_tensor)
+    # print("Code:", vq_code)
+
+    vq_code_fake = torch.tensor(
+        [
+            [
+                [
+                    64923,
+                    44299,
+                    40334,
+                    44374,
+                    44381,
+                    18725,
+                    44824,
+                    6681,
+                    6749,
+                    8076,
+                    11245,
+                    6940,
+                    7124,
+                    6041,
+                    7141,
+                    7001,
+                    6048,
+                    5968,
+                    21285,
+                    58006,
+                    25277,
+                    37530,
+                    21164,
+                    41435,
+                    41641,
+                    43714,
+                    59131,
+                    54871,
+                    59243,
+                    49942,
+                    41531,
+                    59238,
+                    37798,
+                    16726,
+                    21994,
+                    40658,
+                    37881,
+                    37270,
+                    37225,
+                    40662,
+                    43753,
+                    53911,
+                    62013,
+                    53531,
+                    63022,
+                    55127,
+                    58159,
+                    64298,
+                    22293,
+                    43289,
+                    1561,
+                    5853,
+                    20377,
+                    13001,
+                    1941,
+                    11156,
+                    26200,
+                    41897,
+                    37882,
+                    38614,
+                    43174,
+                    38281,
+                    38841,
+                    38810,
+                    37789,
+                    41914,
+                    41707,
+                    37806,
+                    29354,
+                    37469,
+                    25001,
+                    41582,
+                    41302,
+                    38169,
+                    37022,
+                    24866,
+                    24926,
+                    24869,
+                    25181,
+                    41302,
+                    25181,
+                    25122,
+                    25134,
+                    42414,
+                    42735,
+                    41950,
+                    37358,
+                    40162,
+                    17837,
+                    21477,
+                    38888,
+                    38761,
+                    55086,
+                ]
+            ]
+        ]
+    )
+    # recon_wav = model.decode_code(vq_code).cpu()  # Shape: (1, 1, T')
+    recon_wav = model.decode_code(vq_code_fake).cpu()  # Shape: (1, 1, T')
+
+
+sf.write("data/reconstructed2.wav", recon_wav[0, 0, :].numpy(), sr)
+print("Done! Check reconstructed.wav")
+
+```