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---
license: apache-2.0
base_model: unsloth/csm-1b
tags:
- unsloth
- peft
- lora
- text-to-speech
- speech
- audio
library_name: transformers
---
# TurkishCodeMan/csm-1b-lora-fft
Bu repo, `unsloth/csm-1b` taban modeli üzerinde **LoRA (PEFT) fine-tune** ile oluşturulmuş bir adaptördür.
Eğitim, Unsloth + Transformers ile yapılmıştır.
## Model Özeti
- **Base model:** `unsloth/csm-1b`
- **Fine-tuning:** LoRA (PEFT)
- **Örnek kullanım:** Referans ses + metin ile konuşma üretimi (CSM chat template)
> Not: Bu repo LoRA adaptörü içerir. Inference için base model + bu adaptörü birlikte yüklemelisiniz.
## Kurulum
```
pip install -U "transformers>=4.52.0" accelerate peft soundfile
```
## Inference (Referans ses ile)
Aşağıdaki örnek, bir `wav` referans ses ve hedef metin ile audio üretir.
```python
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")
```
## Eğitim Notları
- Audio girişleri 24kHz olarak hazırlanmalıdır (mono önerilir).
- Dataset pipeline’ında sesler sabit uzunluğa pad/trim edilerek batch hataları önlenir.
## Lisans
Base model lisansı ve veri seti lisansı geçerlidir. Bu repo adaptör ağırlıklarını içerir.
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