Create app.py

app.py

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+import numpy as np
+import torch
+import soundfile as sf
+from transformers import AutoProcessor
+from peft import PeftModel
+from transformers import CsmForConditionalGeneration
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+sampling_rate = 24_000
+
+base_id = "unsloth/csm-1b"
+adapter_id = "TurkishCodeMan/csm-1b-lora-fft"
+
+processor = AutoProcessor.from_pretrained(base_id)
+base = CsmForConditionalGeneration.from_pretrained(base_id, torch_dtype="auto").to(device)
+model = PeftModel.from_pretrained(base, adapter_id).to(device)
+model.eval()
+
+def _resample_linear(audio: np.ndarray, orig_sr: int, target_sr: int) -> np.ndarray:
+    if orig_sr == target_sr:
+        return audio
+    if audio.ndim == 2:
+        audio = audio.mean(axis=1)
+    n = audio.shape[0]
+    new_n = int(round(n * (target_sr / orig_sr)))
+    if new_n <= 1:
+        return audio[:1].astype(np.float32)
+    x_old = np.linspace(0.0, 1.0, num=n, endpoint=True)
+    x_new = np.linspace(0.0, 1.0, num=new_n, endpoint=True)
+    return np.interp(x_new, x_old, audio).astype(np.float32)
+
+# Reference audio (wav path)
+ref_path = "reference.wav"
+ref_audio, ref_sr = sf.read(ref_path, dtype="float32")
+if ref_audio.ndim == 2:
+    ref_audio = ref_audio.mean(axis=1).astype(np.float32)
+if ref_sr != sampling_rate:
+    ref_audio = _resample_linear(ref_audio, ref_sr, sampling_rate)
+
+ref_text = "Reference transcript (optional)."
+target_text = "We extend the standard NIAH task, to investigate model behavior in previously underexplored settings."
+
+speaker_role = "0"
+conversation = [
+    {
+        "role": speaker_role,
+        "content": [
+            {"type": "text", "text": "Please speak english\n\n" + ref_text},
+            {"type": "audio", "audio": ref_audio},
+        ],
+    },
+    {
+        "role": speaker_role,
+        "content": [
+            {"type": "text", "text": target_text},
+        ],
+    },
+]
+
+inputs = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(device)
+
+with torch.no_grad():
+    out = model.generate(
+        **inputs,
+        output_audio=True,
+        max_new_tokens=200,
+        depth_decoder_temperature=0.6,
+        depth_decoder_top_k=0,
+        depth_decoder_top_p=0.7,
+        temperature=0.3,
+        top_k=50,
+        top_p=1.0,
+    )
+
+generated_audio = out[0].detach().cpu().to(torch.float32).numpy()
+sf.write("generated_audio.wav", generated_audio, samplerate=sampling_rate)
+print("Wrote generated_audio.wav")