diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index 7196d6ab..5e278978 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -698,6 +698,10 @@ class Text2SemanticDecoder(nn.Module): y_list = [None] * y.shape[0] batch_idx_map = list(range(y.shape[0])) idx_list = [None] * y.shape[0] + bad_tokens_list = [809, 207,411,679,676,25,23,7] + bad_tokens = torch.tensor(bad_tokens_list, device=x.device, dtype=torch.long) + last_2_token = torch.full((bsz,), -1, dtype=torch.long, device=x.device) + repeat_count = torch.zeros(bsz, dtype=torch.float, device=x.device) for idx in tqdm(range(1500)): if idx == 0: xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, attn_mask, None) @@ -710,7 +714,66 @@ class Text2SemanticDecoder(nn.Module): logits = logits[:, :-1] else: attn_mask = F.pad(attn_mask, (0, 1), value=False) + # 1. 获取上一步生成的 token + last_token = y[:, -1] + # 2. 检查对于批次中的每个序列，其上一步生成的token是否已经重复，如果重复，降低选中概率 + is_repeated = (last_token == last_2_token) + repeat_count[is_repeated] += 1 + # 3. 对没有重复的序列，计数器清零 + repeat_count[~is_repeated] = 0 # ~是布尔 'not' 操作 + if is_repeated.any(): + # 获取重复序列的行索引 + repeated_rows = torch.where(is_repeated)[0] + + # 获取这些重复序列对应的 token ID + repeated_tokens = last_2_token[repeated_rows] + + # 获取这些重复 token 当前的 logits 值 + # 这是一种 "gather" 操作，精确选取 logits[row, token_id] + current_logits_values = logits[repeated_rows, repeated_tokens] + + # 获取这些重复序列的重复次数 + current_repeat_counts = repeat_count[repeated_rows] + + # --- 创建掩码来模拟 if/elif/else --- + # 条件1: 0 < logit < 35 + mask1 = (current_logits_values > 0) & (current_logits_values < 35) + # 条件2: logit < 0 + mask2 = current_logits_values < 0 + # 条件3: logit >= 35 + mask3 = current_logits_values >= 35 + + # 4. --- 应用惩罚 --- + # 对满足条件1的 logits 应用惩罚 + if mask1.any(): + rows_to_update = repeated_rows[mask1] + tokens_to_update = repeated_tokens[mask1] + counts_to_use = current_repeat_counts[mask1] + logits[rows_to_update, tokens_to_update] *= (0.618 ** counts_to_use.unsqueeze(1)).squeeze(1) + + # 对满足条件2的 logits 应用惩罚 + if mask2.any(): + rows_to_update = repeated_rows[mask2] + tokens_to_update = repeated_tokens[mask2] + counts_to_use = current_repeat_counts[mask2] + logits[rows_to_update, tokens_to_update] *= (1.414 ** counts_to_use.unsqueeze(1)).squeeze(1) + + # 对满足条件3的 logits 应用惩罚 + if mask3.any(): + rows_to_update = repeated_rows[mask3] + tokens_to_update = repeated_tokens[mask3] + logits[rows_to_update, tokens_to_update] = -float('inf') # 你的逻辑是 *= 0，设为 -inf 更能禁止采样 + + # 5. 更新 last_2_token 以备下一次迭代 + # 使用 .clone() 以免在下一次循环中意外修改 last_token + last_2_token = last_token.clone() + + # 6. 处理 bad_tokens 列表 + is_last_token_bad = torch.isin(last_token, bad_tokens) + if is_last_token_bad.any(): + bad_rows = is_last_token_bad.nonzero(as_tuple=True)[0] + logits[bad_rows.unsqueeze(1), bad_tokens] = -float('inf') samples = sample( logits, y, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature )[0] @@ -874,7 +937,10 @@ class Text2SemanticDecoder(nn.Module): .view(bsz, self.num_head, src_len, src_len) .to(device=x.device, dtype=torch.bool) ) - + bad_tokens_list = [809, 207,411,679,676,25,23,7] + bad_tokens = torch.tensor(bad_tokens_list, device=x.device, dtype=torch.long) + last_2_token = torch.full((bsz,), -1, dtype=torch.long, device=x.device) + repeat_count = torch.zeros(bsz, dtype=torch.float, device=x.device) for idx in tqdm(range(1500)): if xy_attn_mask is not None: xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None) @@ -887,7 +953,66 @@ class Text2SemanticDecoder(nn.Module): xy_attn_mask = None if idx < 11: ###至少预测出10个token不然不给停止（0.4s） logits = logits[:, :-1] + # 1. 获取上一步生成的 token + last_token = y[:, -1] + # 2. 检查对于批次中的每个序列，其上一步生成的token是否已经重复，如果重复，降低选中概率 + is_repeated = (last_token == last_2_token) + repeat_count[is_repeated] += 1 + # 3. 对没有重复的序列，计数器清零 + repeat_count[~is_repeated] = 0 # ~是布尔 'not' 操作 + if is_repeated.any(): + # 获取重复序列的行索引 + repeated_rows = torch.where(is_repeated)[0] + + # 获取这些重复序列对应的 token ID + repeated_tokens = last_2_token[repeated_rows] + + # 获取这些重复 token 当前的 logits 值 + # 这是一种 "gather" 操作，精确选取 logits[row, token_id] + current_logits_values = logits[repeated_rows, repeated_tokens] + + # 获取这些重复序列的重复次数 + current_repeat_counts = repeat_count[repeated_rows] + + # --- 创建掩码来模拟 if/elif/else --- + # 条件1: 0 < logit < 35 + mask1 = (current_logits_values > 0) & (current_logits_values < 35) + # 条件2: logit < 0 + mask2 = current_logits_values < 0 + # 条件3: logit >= 35 + mask3 = current_logits_values >= 35 + + # 4. --- 应用惩罚 --- + # 对满足条件1的 logits 应用惩罚 + if mask1.any(): + rows_to_update = repeated_rows[mask1] + tokens_to_update = repeated_tokens[mask1] + counts_to_use = current_repeat_counts[mask1] + logits[rows_to_update, tokens_to_update] *= (0.618 ** counts_to_use.unsqueeze(1)).squeeze(1) + + # 对满足条件2的 logits 应用惩罚 + if mask2.any(): + rows_to_update = repeated_rows[mask2] + tokens_to_update = repeated_tokens[mask2] + counts_to_use = current_repeat_counts[mask2] + logits[rows_to_update, tokens_to_update] *= (1.414 ** counts_to_use.unsqueeze(1)).squeeze(1) + + # 对满足条件3的 logits 应用惩罚 + if mask3.any(): + rows_to_update = repeated_rows[mask3] + tokens_to_update = repeated_tokens[mask3] + logits[rows_to_update, tokens_to_update] = -float('inf') # 你的逻辑是 *= 0，设为 -inf 更能禁止采样 + + # 5. 更新 last_2_token 以备下一次迭代 + # 使用 .clone() 以免在下一次循环中意外修改 last_token + last_2_token = last_token.clone() + + # 6. 处理 bad_tokens 列表 + is_last_token_bad = torch.isin(last_token, bad_tokens) + if is_last_token_bad.any(): + bad_rows = is_last_token_bad.nonzero(as_tuple=True)[0] + logits[bad_rows.unsqueeze(1), bad_tokens] = -float('inf') samples = sample( logits, y, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature )[0]