Merge pull request #453 from FunAudioLLM/dev/lyuxiang.lx

cosyvoice/cli/frontend.py

@@ -118,10 +118,10 @@ class CosyVoiceFrontEnd:
             text = text.replace("\n", "")
             text = replace_blank(text)
             text = replace_corner_mark(text)
-            text = text.replace(".", "、")
+            text = text.replace(".", "。")
             text = text.replace(" - ", "，")
             text = remove_bracket(text)
-            text = re.sub(r'[，,]+$', '。', text)
+            text = re.sub(r'[，,、]+$', '。', text)
             texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "zh", token_max_n=80,
                                          token_min_n=60, merge_len=20, comma_split=False))
         else:

cosyvoice/cli/model.py

@@ -31,8 +31,8 @@ class CosyVoiceModel:
         self.llm = llm
         self.flow = flow
         self.hift = hift
-        self.token_min_hop_len = 100
-        self.token_max_hop_len = 200
+        self.token_min_hop_len = 2 * self.flow.input_frame_rate
+        self.token_max_hop_len = 4 * self.flow.input_frame_rate
         self.token_overlap_len = 20
         # mel fade in out
         self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256)
@@ -87,10 +87,7 @@ class CosyVoiceModel:
                                         prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
                                         prompt_speech_token=llm_prompt_speech_token.to(self.device),
                                         prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
-                                        embedding=llm_embedding.to(self.device).half(),
-                                        sampling=25,
-                                        max_token_text_ratio=30,
-                                        min_token_text_ratio=3):
+                                        embedding=llm_embedding.to(self.device).half()):
                 self.tts_speech_token_dict[uuid].append(i)
         self.llm_end_dict[uuid] = True
 

cosyvoice/llm/llm.py

@@ -197,7 +197,7 @@ class TransformerLM(torch.nn.Module):
         offset = 0
         att_cache, cnn_cache = torch.zeros((0, 0, 0, 0), device=lm_input.device), torch.zeros((0, 0, 0, 0), device=lm_input.device)
         for i in range(max_len):
-            y_pred, att_cache, cnn_cache = self.llm.forward_chunk(lm_input, offset=0, required_cache_size=-1,
+            y_pred, att_cache, cnn_cache = self.llm.forward_chunk(lm_input, offset=offset, required_cache_size=-1,
                                                                   att_cache=att_cache, cnn_cache=cnn_cache,
                                                                   att_mask=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]),
                                                                                                  device=lm_input.device)).to(torch.bool))

cosyvoice/utils/frontend_utils.py

@@ -80,6 +80,13 @@ def split_paragraph(text: str, tokenize, lang="zh", token_max_n=80, token_min_n=
         pounc = ['.', '?', '!', ';', ':']
     if comma_split:
         pounc.extend(['，', ','])
+
+    if text[-1] not in pounc:
+        if lang == "zh":
+            text += "。"
+        else:
+            text += "."
+
     st = 0
     utts = []
     for i, c in enumerate(text):
@@ -92,11 +99,7 @@ def split_paragraph(text: str, tokenize, lang="zh", token_max_n=80, token_min_n=
                 st = i + 2
             else:
                 st = i + 1
-    if len(utts) == 0:
-        if lang == "zh":
-            utts.append(text + '。')
-        else:
-            utts.append(text + '.')
+
     final_utts = []
     cur_utt = ""
     for utt in utts:

examples/libritts/cosyvoice/conf/cosyvoice.fromscratch.yaml

@@ -18,7 +18,7 @@ llm: !new:cosyvoice.llm.llm.TransformerLM
     text_encoder_input_size: !ref <text_encoder_input_size>
     llm_input_size: !ref <llm_input_size>
     llm_output_size: !ref <llm_output_size>
-    text_token_size: 51866
+    text_token_size: 51866 # change to 60515 if you want to train with CosyVoice-300M-25Hz recipe
     speech_token_size: 4096
     length_normalized_loss: True
     lsm_weight: 0
@@ -66,7 +66,7 @@ flow: !new:cosyvoice.flow.flow.MaskedDiffWithXvec
     spk_embed_dim: !ref <spk_embed_dim>
     output_type: 'mel'
     vocab_size: 4096
-    input_frame_rate: 50
+    input_frame_rate: 50 # change to 25 if you want to train with CosyVoice-300M-25Hz recipe
     only_mask_loss: True
     encoder: !new:cosyvoice.transformer.encoder.ConformerEncoder
         output_size: 512
@@ -135,7 +135,7 @@ hift: !new:cosyvoice.hifigan.generator.HiFTGenerator
 
 # processor functions
 parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener
-get_tokenizer: !name:whisper.tokenizer.get_tokenizer
+get_tokenizer: !name:whisper.tokenizer.get_tokenizer # change to !name:cosyvoice.tokenizer.tokenizer.get_tokenizer if you want to train with CosyVoice-300M-25Hz recipe
     multilingual: True
     num_languages: 100
     language: 'en'

examples/libritts/cosyvoice/conf/cosyvoice.yaml

@@ -18,7 +18,7 @@ llm: !new:cosyvoice.llm.llm.TransformerLM
     text_encoder_input_size: !ref <text_encoder_input_size>
     llm_input_size: !ref <llm_input_size>
     llm_output_size: !ref <llm_output_size>
-    text_token_size: 51866
+    text_token_size: 51866 # change to 60515 if you want to train with CosyVoice-300M-25Hz recipe
     speech_token_size: 4096
     length_normalized_loss: True
     lsm_weight: 0
@@ -66,7 +66,7 @@ flow: !new:cosyvoice.flow.flow.MaskedDiffWithXvec
     spk_embed_dim: !ref <spk_embed_dim>
     output_type: 'mel'
     vocab_size: 4096
-    input_frame_rate: 50
+    input_frame_rate: 50 # change to 25 if you want to train with CosyVoice-300M-25Hz recipe
     only_mask_loss: True
     encoder: !new:cosyvoice.transformer.encoder.ConformerEncoder
         output_size: 512
@@ -135,7 +135,7 @@ hift: !new:cosyvoice.hifigan.generator.HiFTGenerator
 
 # processor functions
 parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener
-get_tokenizer: !name:whisper.tokenizer.get_tokenizer
+get_tokenizer: !name:whisper.tokenizer.get_tokenizer # change to !name:cosyvoice.tokenizer.tokenizer.get_tokenizer if you want to train with CosyVoice-300M-25Hz recipe
     multilingual: True
     num_languages: 100
     language: 'en'

examples/magicdata-read/cosyvoice/conf/cosyvoice.fromscratch.yaml

@@ -18,7 +18,7 @@ llm: !new:cosyvoice.llm.llm.TransformerLM
     text_encoder_input_size: !ref <text_encoder_input_size>
     llm_input_size: !ref <llm_input_size>
     llm_output_size: !ref <llm_output_size>
-    text_token_size: 51866
+    text_token_size: 51866 # change to 60515 if you want to train with CosyVoice-300M-25Hz recipe
     speech_token_size: 4096
     length_normalized_loss: True
     lsm_weight: 0
@@ -54,6 +54,11 @@ llm: !new:cosyvoice.llm.llm.TransformerLM
         pos_enc_layer_type: 'rel_pos_espnet'
         selfattention_layer_type: 'rel_selfattn'
         static_chunk_size: 1
+    sampling: !name:cosyvoice.utils.common.ras_sampling
+        top_p: 0.8
+        top_k: 25
+        win_size: 10
+        tau_r: 0.1
 
 flow: !new:cosyvoice.flow.flow.MaskedDiffWithXvec
     input_size: 512
@@ -61,7 +66,7 @@ flow: !new:cosyvoice.flow.flow.MaskedDiffWithXvec
     spk_embed_dim: !ref <spk_embed_dim>
     output_type: 'mel'
     vocab_size: 4096
-    input_frame_rate: 50
+    input_frame_rate: 50 # change to 25 if you want to train with CosyVoice-300M-25Hz recipe
     only_mask_loss: True
     encoder: !new:cosyvoice.transformer.encoder.ConformerEncoder
         output_size: 512
@@ -130,7 +135,7 @@ hift: !new:cosyvoice.hifigan.generator.HiFTGenerator
 
 # processor functions
 parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener
-get_tokenizer: !name:whisper.tokenizer.get_tokenizer
+get_tokenizer: !name:whisper.tokenizer.get_tokenizer # change to !name:cosyvoice.tokenizer.tokenizer.get_tokenizer if you want to train with CosyVoice-300M-25Hz recipe
     multilingual: True
     num_languages: 100
     language: 'en'

examples/magicdata-read/cosyvoice/conf/cosyvoice.yaml

@@ -18,7 +18,7 @@ llm: !new:cosyvoice.llm.llm.TransformerLM
     text_encoder_input_size: !ref <text_encoder_input_size>
     llm_input_size: !ref <llm_input_size>
     llm_output_size: !ref <llm_output_size>
-    text_token_size: 51866
+    text_token_size: 51866 # change to 60515 if you want to train with CosyVoice-300M-25Hz recipe
     speech_token_size: 4096
     length_normalized_loss: True
     lsm_weight: 0
@@ -54,6 +54,11 @@ llm: !new:cosyvoice.llm.llm.TransformerLM
         pos_enc_layer_type: 'rel_pos_espnet'
         selfattention_layer_type: 'rel_selfattn'
         static_chunk_size: 1
+    sampling: !name:cosyvoice.utils.common.ras_sampling
+        top_p: 0.8
+        top_k: 25
+        win_size: 10
+        tau_r: 0.1
 
 flow: !new:cosyvoice.flow.flow.MaskedDiffWithXvec
     input_size: 512
@@ -61,7 +66,7 @@ flow: !new:cosyvoice.flow.flow.MaskedDiffWithXvec
     spk_embed_dim: !ref <spk_embed_dim>
     output_type: 'mel'
     vocab_size: 4096
-    input_frame_rate: 50
+    input_frame_rate: 50 # change to 25 if you want to train with CosyVoice-300M-25Hz recipe
     only_mask_loss: True
     encoder: !new:cosyvoice.transformer.encoder.ConformerEncoder
         output_size: 512
@@ -130,7 +135,7 @@ hift: !new:cosyvoice.hifigan.generator.HiFTGenerator
 
 # processor functions
 parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener
-get_tokenizer: !name:whisper.tokenizer.get_tokenizer
+get_tokenizer: !name:whisper.tokenizer.get_tokenizer # change to !name:cosyvoice.tokenizer.tokenizer.get_tokenizer if you want to train with CosyVoice-300M-25Hz recipe
     multilingual: True
     num_languages: 100
     language: 'en'