Fix label alignment bug in finetuning

[matevosashot]

Fix label alignment bug in finetuning

Fixes label alignment issues in sft_12hz.py and modeling_qwen3_tts.py caused by incorrect interaction with ForCausalLMLoss from transformers.

Context

ForCausalLMLoss has two modes: passing labels automatically shifts them left by one (token n predicts n+1), while passing shift_labels uses them as-is.

sft_12hz.py

The old code manually shifted inputs and labels before passing to the talker (inputs_embeds[:, :-1], labels[:, 1:]). This is unnecessary — ForCausalLMLoss already handles the left-shift internally. The fix passes full unshifted tensors and adjusts hidden state slicing accordingly. Also adds the missing text_projection call on text embeddings.

modeling_qwen3_tts.py

The subtalker outputs 15 codes per position. Passing them via labels causes ForCausalLMLoss to shift and drop one, leaving only 14 — misaligning with the 15 logit outputs. The fix passes subtalker labels via shift_labels instead, bypassing the automatic shift.

---

ForCausalLMLoss implementation:

def ForCausalLMLoss(
    logits, labels, vocab_size,
    num_items_in_batch=None, ignore_index=-100,
    shift_labels=None, **kwargs,
) -> torch.Tensor:
    logits = logits.float()
    if shift_labels is None:
        # Shift so that tokens < n predict n
        labels = nn.functional.pad(labels, (0, 1), value=ignore_index)
        shift_labels = labels[..., 1:].contiguous()
    logits = logits.view(-1, vocab_size)
    shift_labels = shift_labels.view(-1)
    shift_labels = shift_labels.to(logits.device)
    loss = fixed_cross_entropy(logits, shift_labels, num_items_in_batch, ignore_index, **kwargs)
    return loss