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Merge 7338302ee4381be81acf6cf5ce7e3449a5974472 into 11aa78bd9bda8b53047cfcae03abf7ca94d27391

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MiaoMiao Li 2025-11-03 07:23:30 +00:00 committed by GitHub
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GPT_SoVITS/AR/models/t2s_model.py
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@ -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]