kevinwang676 / RWKV-TTS

RWKVTTS

This project is to train an RWKV LLM for TTS generation which compatible to other TTS engine(like fish/cosy/chattts).

For most of modern LLM based TTS engine, there are two parts :

1. VQ VAE: this model is to encode audio to audio tokens and decode audio tokens to audio.
2. LLM: this model is to generate audio tokens using text tokens and the prompt audio tokens. The prompt audio tokens are also from VQ VAE.

Typically the training of the LLM based TTS involves VQ-VAE training and LLM training, like CosyTTS, ChatTTS and FishTTS. However we focus to train an RWKV LLM model to replace the LLM part in these TTS engines.

flowchart TB
  node_1[["Input Prompt Text"]]
  node_2(["Text Tokenizer"])
  node_3(["Audio Tokenizer(VQ)"])
  node_4[["Input Reference Audio"]]
  node_5[["Text Tokens"]]
  node_6[["Audio Tokens"]]
  node_7(["Text Embedder"])
  node_8(["Audio Embedder"])
  node_9[["Text Embeddings"]]
  node_10[["Audio Embeddings"]]
  node_11(["Concatenate Embeddings"])
  node_12[["Input Embeddings"]]
  node_13{{"Language Model"}}
  node_14[["Hidden States"]]
  node_15(["Audio Head"])
  node_16{"Continue to decode?"}
  node_17(["Next Step Input"])
  node_18(["Finish Decode"])
  node_1 --> node_2
  node_4 --> node_3
  node_2 --> node_5
  node_3 --> node_6
  node_5 --> node_7
  node_6 --> node_8
  node_7 --> node_9
  node_8 --> node_10
  node_9 --> node_11
  node_10 --> node_11
  node_11 --> node_12
  node_12 --> node_13
  node_13 --> node_14
  node_14 --> node_15
  node_15 --> node_16
  node_16 --"Yes"--> node_17
  node_17 --> node_13
  node_16 --"No"--> node_18

Different TTS engines might have different data layout and different special control token, so we need to prepare different data and train a RWKV LLM model for each TTS engine.

Process to train LLM for different TTS engine

Cosy 2.0

Cosy 2.0 Data Layout

The layout of Cosy 2.0 for LLM:

flowchart LR
  node_1[["SOS Embeddings"]]
  node_2[["Text Embeddings"]]
  node_3[["Task ID Embedings"]]
  node_4[["Audio Embeddings"]]
  node_5[["Last Audio Embeddings"]]
  node_1 --- node_2
  node_2 --- node_3
  node_3 --> node_4
  node_4 --> node_5

The forward of LLM for cosy 2.0:

graph TD
    A[Input: batch] --> B[Extract tokens and lengths]
    B --> C1[Prepare LLM Target]
    B --> C2[Encode Text Tokens]
    B --> C3[Generate SOS/EOS and Task ID Embeddings]
    B --> C4[Encode Speech Tokens]
    
    C1[Prepare LLM Target] --> D1["Create target sequence for each sample<br>[IGNORE_ID, ..., speech_tokens, EOS]"]
    D1 --> D2[Pad and move target to device]
    
    C2[Encode Text Tokens] --> E1[Apply text_embedding layer]
    
    C3[Generate SOS/EOS and Task ID Embeddings] --> F1[Get SOS/EOS embeddings from llm_embedding]
    C3 --> F2[Get task_id embeddings from llm_embedding]
    
    C4[Encode Speech Tokens] --> G1[Apply speech_embedding layer]
    
    E1 --> H[Unpad and pad sequence]
    F1 --> H
    F2 --> H
    G1 --> H
    
    H --> I1[Generate LM input]
    H --> I2[Create attention mask]
    
    I1 --> J[Run LLM forward pass]
    I2 --> J
    
    J --> K[Extract hidden states]
    K --> L[Generate logits through llm_decoder]
    
    D2 --> M[Compute loss and accuracy]
    L --> M
    
    M --> N[Return loss and accuracy]

There are some points to note for Cosy 2.0:

1. The prompt audio tokens are used to act reference audio, LLM will generate audio tokens mimic the reference audio.
2. '<|endofprompt|>' is used for prompt text, it is a special token to indicate this prompt is an instruction.

Cosy 2.0 Data Preparation

1. Download reference audio files from https://huggingface.co/datasets/yueyulin/TTS_Reference and put them to folder $REF_AUDIO_DIR. These audios are used to generate audio tokens.
2. Download Cosy 2.0-0.5B model from https://huggingface.co/FunAudioLLM/CosyVoice2-0.5B and put it to folder $MODEL_DIR.
3. Clone the Cosy 2.0 repo from: https://github.com/yynil/CosyVoice and follow the instruction to install the environment. In this repository, I change the codes to allow user to specify cuda device for multiple processes generation. If you have installed torch 2.6, please remember to force triton downgrading to 3.1.0.
4. Prepare the text data for audio tokens's training dataset. Currently we support parquet files and jsonl files. The text field is the only required field in the data file. I download the parquet from wikipedia for Chinese and Engish parquet files.
5. Generate the audio tokens using the following command:

bash run_multiple_process.sh --parquet_files /home/yueyulin/data/wiki/zh/train-00000-of-00006.parquet /home/yueyulin/data/wiki/zh/train-00001-of-00006.parquet /home/yueyulin/data/wiki/zh/train-00002-of-00006.parquet /home/yueyulin/data/wiki/zh/train-00003-of-00006.parquet /home/yueyulin/data/wiki/zh/train-00004-of-00006.parquet /home/yueyulin/data/wiki/zh/train-00005-of-00006.parquet --language zh --prompts_dir extract_data/prompts/zh --device cuda:0 --output_dir /home/yueyulin/data/speech_corpus

The prompts_dir is the $REF_AUDIO_DIR, the parquet_files are the list of files downloaded from wikimedia, each file is processed by one file. In my experience, one 4090 can process 6 files at the same time. The output_dir is the dirctory that audio tokens generated and saved.

Cosy 2.0 LLM Training

After data is generated and saved, we will get the JSONL files like :

{"text": "甄别重点监测企业是确保监测数据全面性和代表性的基础。首先，需要根据预警机制的覆盖范围和目标，明确监测企业的选择标准。选择标准可以包括企业规模、市场份额、行业影响力等。其次，通过企业调查、行业协会推荐等方式，初步筛选出符合条件的潜在监测企业。", "tts_speech_tokens": [2031, 4137, 6405, 6405, 6405, 6405, 6405, 6324, 6324, 6324, 6324, 6324, 6324, 4218, 1761, 4509, 2333, 4483, 5934, 6258, 1929, 3482, 314, 2300, 957, 5163, 6309, 5064, 6425, 3992, 1932, 80, 305, 734, 1479, 5650, 2472, 4778, 4487, 6175, 5667, 5373, 2187, 4851, 137, 141, 4919, 4407, 2436, 1295, 2024, 1294, 4940, 4778, 2330, 764, 1762, 2031, 1788, 5943, 5319, 5238, 5338, 3872, 1614, 4920, 6055, 6027, 3084, 5343, 4605, 2330, 218, 2172, 572, 1949, 1331, 865, 4921, 2472, 4688, 4379, 5850, 6342, 6373, 2997, 2529, 5087, 623, 3700, 6292, 6291, 5823, 5830, 2102, 1041, 6225, 6316, 3887, 889, 5487, 3813, 1626, 953, 734, 909, 4314, 4804, 4821, 4463, 23, 4683, 4678, 2724, 4832, 992, 1238, 2673, 324, 2099, 2486, 135, 2001, 4537, 5271, 2519, 957, 1699, 953, 1304, 1028, 4752, 2553, 5560, 4154, 1287, 59, 879, 4921, 2499, 5748, 5019, 240, 5889, 6264, 4293, 2186, 2105, 2005, 6405, 6405, 6324, 6324, 6324, 4137, 4218, 3651, 6048, 3132, 1433, 1457, 3962, 4515, 2482, 4490, 4561, 4669, 6054, 6270, 6316, 4615, 4781, 575, 632, 2031, 183, 4598, 4479, 6181, 5496, 4128, 3887, 1943, 1861, 6288, 5343, 6072, 3319, 2733, 322, 1187, 1727, 1807, 4921, 4677, 5668, 5019, 2427, 2976, 6066, 5332, 63, 73, 380, 4239, 6534, 6543, 5101, 1452, 213, 5921, 2273, 6453, 4347, 4537, 4459, 11, 2124, 866, 386, 485, 2511, 333, 632, 4317, 5772, 5803, 1457, 2163, 889, 5021, 2381, 5675, 5056, 5092, 1951, 3888, 3645, 4218, 6405, 6324, 4137, 1884, 1646, 2726, 377, 3992, 5529, 2481, 6054, 3822, 5340, 2330, 71, 2733, 2499, 5012, 4463, 5850, 6342, 6373, 2268, 4851, 137, 151, 4921, 4435, 4650, 528, 1295, 1295, 2023, 2753, 4850, 4570, 2243, 1047, 56, 113, 4512, 5568, 1662, 971, 5, 1480, 6387, 1045, 65, 460, 2160, 5102, 4568, 5056, 5098, 1602, 6048, 4367, 956, 59, 1524, 6405, 6405, 6324, 6324, 6324, 6324, 6324, 4137, 2031, 2706, 5325, 1653, 3887, 2219, 3667, 5664, 803, 4592, 2163, 5587, 4598, 5026, 5089, 1692, 5976, 1937, 146, 41, 1507, 1950, 2031, 0, 2349, 343, 4607, 5019, 566, 1683, 2166, 5051, 5678, 5057, 5830, 573, 2835, 2856, 5099, 707, 947, 1113, 4675, 4408, 4623, 1294, 2024, 2023, 3481, 4778, 2411, 1208, 1302, 660, 5827, 5345, 5074, 4560, 6501, 1403, 635, 716, 680, 5057, 4970, 1947, 3645, 1458, 1707, 6024, 6049, 5238, 5340, 1696, 5244, 1468, 1946, 509, 1318, 6534, 2800, 4510, 2234, 1991, 2017, 2018, 1370, 470, 2891, 4997, 1972, 1701, 5832, 1458, 1950, 4860, 5589, 1946, 1949, 509, 5369, 4966, 5019, 4849, 2411, 314, 1293, 1267, 377, 6421, 4800, 4416, 4893, 8, 1946, 1967, 1584, 4615, 5019, 2510, 867, 63, 245, 533, 1991, 4218, 6405, 6405, 6324, 6324, 6324, 6324, 6324, 4137, 1950, 4920, 4516, 276, 2024, 4777, 4194, 6373, 5643, 4851, 4448, 65, 1517, 1978, 4218, 6405, 4218, 2112, 1350, 4860, 5074, 5772, 6262, 672, 5097, 5090, 221, 1032, 4675, 4408, 285, 1295, 1294, 557, 4490, 228, 276, 4858, 4807, 2870, 1675, 6051, 1539, 4141, 1946, 4133, 6320, 4699, 982, 1950, 5832, 5835, 3645, 1947, 5589, 5589, 4136, 1946, 1235, 4642, 4993, 4857, 4598, 62, 4431, 4675, 285, 1043, 314, 2414, 2760, 2850, 5094, 3158, 1214, 1032, 2997, 2763, 5345, 5100, 402, 4677, 4857, 4543, 5, 1482, 2004, 56, 515, 1970, 2077, 6534, 3488, 5591, 5690, 5869, 5319, 2331, 5342, 1688, 1679, 1735, 4218, 6324, 6324, 6405, 4218, 2031, 5886, 6291, 6480, 2883, 5829, 5826, 2175, 5799, 5826, 2186, 2183, 5940, 5322, 120, 5918, 4571, 4687, 3813, 962, 737, 1561, 5886, 4077, 1429, 5831, 6560, 3644, 6429, 6507, 6534, 2101, 2186, 5097, 2682, 2673, 2017, 2576, 4594, 1005, 4785, 2760, 854, 1946, 683, 4844, 2733, 4695, 4840, 2192, 1482, 72, 29, 788, 1761, 4921, 4408, 2517, 566, 35, 2192, 5934, 4209, 5652, 4537, 5920, 278, 160, 3462, 4686, 5021, 4490, 5853, 3912, 6374, 2997, 4716, 2567, 140, 3462, 4435, 2436, 1295, 1295, 2023, 3482, 4769, 4598, 89, 1736, 4218, 6405, 6405, 6324, 6324, 4137], "prompt_text": "那么就在两侧的象限同时忙碌。", "llm_prompt_speech_token": [3686, 6324, 4137, 1959, 3666, 4376, 2836, 2127, 578, 2441, 1041, 2337, 6073, 3560, 1369, 5650, 4691, 5192, 2924, 89, 1687, 1539, 4218, 1848, 160, 4760, 2825, 1463, 1946, 1223, 1313, 2067, 5648, 2997, 2268, 2277, 4842, 4763, 308, 1038, 140, 842, 2983, 4672, 4650, 4696, 5995, 5603, 1238, 1238, 4672, 4650, 4777, 2474, 8, 767, 1731, 4299, 2079, 4941, 4947, 665, 719, 4319, 6424, 5067, 5967, 6048, 5967, 5238, 1523, 3875, 3872, 4314, 661, 1946, 1217, 500, 6422, 1506, 4852, 5831, 1457, 1448]}
{"text": "Once all the Cabinet and Cabinet-level officers have been invested, the act of their investiture usually ends with a \"family photo\" of the new Administration around the new president and vice-president. For this photo, the new ministers' alignment and proximity to the president is dictated by the order of precedence, with the ministers who head older departments standing in the first row, and the heads of the newer departments standing in the back rows. Some departments, such as the Department of Defence, take precedence from prior departments now abolished.", "tts_speech_tokens": [764, 35, 1896, 4299, 6486, 4299, 4299, 4299, 4218, 651, 2112, 2131, 1403, 2792, 2207, 1725, 5401, 281, 575, 683, 4997, 3474, 4492, 195, 87, 5109, 5846, 6077, 2270, 2172, 3828, 4424, 4543, 1520, 1753, 6258, 4075, 141, 5109, 5845, 3647, 1188, 3987, 3750, 4414, 1516, 4180, 5014, 5348, 1441, 6534, 5075, 5100, 1274, 1301, 3569, 3488, 3996, 6183, 4752, 4919, 2328, 3158, 6071, 5264, 5482, 5403, 5844, 5837, 191, 2139, 1839, 2255, 831, 4508, 4576, 6255, 1857, 29, 2, 2228, 5482, 6459, 2004, 2253, 2267, 2255, 885, 2112, 1788, 5916, 5835, 5919, 5919, 5919, 4056, 4299, 2058, 2982, 1295, 305, 1463, 3647, 2383, 2112, 3054, 4603, 3043, 4272, 2260, 4841, 6029, 6062, 5329, 6256, 6465, 2386, 2921, 2204, 4429, 5647, 2085, 2490, 809, 159, 546, 5325, 5298, 917, 1688, 3863, 3872, 3884, 3481, 3480, 4130, 5993, 5979, 5322, 5257, 5634, 4691, 4533, 5100, 1277, 764, 5111, 5, 47, 3748, 4929, 2376, 3583, 2990, 6456, 2232, 2306, 6507, 6210, 4463, 5840, 2270, 4071, 5693, 4663, 5100, 5226, 6510, 6534, 2900, 2567, 137, 882, 1199, 2831, 632, 389, 4251, 4191, 73, 49, 3831, 404, 971, 4853, 4613, 4074, 4314, 2417, 3750, 4507, 4416, 4594, 3624, 5325, 962, 224, 404, 5295, 4596, 2238, 3670, 3848, 4339, 1676, 812, 2441, 6097, 3934, 2261, 3750, 1564, 3401, 6074, 5823, 1383, 4293, 3816, 3734, 2219, 4450, 5482, 2996, 150, 3063, 143, 3019, 3667, 149, 3748, 4278, 4347, 3485, 5270, 4858, 5239, 2568, 2028, 4050, 3011, 32, 2264, 4672, 2991, 888, 804, 149, 2234, 5934, 1744, 2112, 3975, 5916, 5943, 5919, 5943, 5919, 5946, 5916, 3972, 4299, 6402, 6534, 1927, 140, 1038, 2263, 4567, 4413, 5563, 4672, 3999, 6264, 4826, 2810, 2567, 228, 227, 2324, 2504, 1773, 6375, 77, 3831, 754, 3401, 4612, 6498, 4311, 2411, 831, 2255, 4414, 5320, 4920, 2328, 5345, 5169, 4752, 4763, 5014, 6449, 2687, 3413, 3647, 2276, 3670, 4069, 1883, 2330, 4499, 1525, 1762, 1490, 2921, 1639, 2166, 4050, 4304, 2837, 732, 6049, 5405, 2266, 910, 4315, 2399, 798, 4859, 4857, 1923, 4434, 4485, 5152, 4206, 4447, 1917, 2136, 3807, 3740, 5, 2264, 5166, 5409, 806, 2982, 878, 2258, 860, 1525, 1762, 3320, 5169, 2166, 546, 2994, 4526, 4056, 2112, 60, 2274, 2528, 5084, 231, 4450, 4597, 1938, 2163, 650, 5108, 2335, 4188, 4859, 1760, 2096, 2903, 4349, 1684, 873, 3872, 6059, 6058, 5976, 4299, 2136, 4050, 3740, 2, 4432, 6455, 2226, 886, 3063, 881, 71, 2234, 5937, 5650, 5238, 4296, 1422, 2342, 2139, 3462, 2261, 1641, 4314, 230, 186, 2965, 4523, 4509, 4999, 4839, 5345, 6070, 5263, 4839, 3813, 3018, 5825, 2926, 5106, 2924, 194, 147, 1433, 728, 2915, 477, 2325, 5330, 6070, 1527, 2421, 2166, 3564, 6166, 1865, 1676, 2092, 4068, 2255, 1483, 5658, 5726, 2085, 3219, 71, 35, 2219, 3828, 2210, 5047, 6100, 4526, 2934, 3909, 4511, 6453, 6534, 3367, 3863, 3146, 5241, 5323, 6054, 1872, 3881, 947, 380, 632, 2909, 2884, 4296, 5913, 5835, 5919, 5919, 5919, 5838, 3975, 2112, 3648, 2192, 831, 3906, 2222, 5118, 5111, 4487, 879, 5650, 4422, 5256, 6465, 4446, 4522, 3831, 2294, 5588, 5825, 3377, 6050, 1698, 147, 1920, 1404, 6328, 1622, 1676, 2083, 2124, 2336, 3669, 5402, 4269, 2490, 71, 8, 113, 1563, 395, 4238, 2510, 3016, 3936, 4430, 2163, 461, 5192, 5998, 5272, 1869, 651, 4302, 1685, 221, 380, 389, 803, 5412, 4753, 2244, 2028, 3648, 3729, 5916, 5919, 5916, 3732, 3975, 2112, 3894, 5239, 5648, 2250, 2918, 4807, 6258, 879, 4600, 2166, 3483, 6327, 6239, 1652, 1757, 1881, 128, 2264, 5935, 5631, 5729, 5482, 2198, 2309, 1329, 4756, 2263, 4448, 4437, 6454, 4272, 3465, 157, 66, 954, 2166, 5598, 3980, 3836, 1838, 2064, 4069, 2371, 2938, 4565, 4356, 789, 4612, 5940, 6510, 3270, 5, 737, 8, 2234, 3747, 5650, 5482, 4269, 303, 2193, 2447, 4849, 2112, 2085, 4050, 3739, 2192, 4428, 5486, 2253, 885, 2992, 2249, 5205, 3453, 4672, 6186, 6534, 6059, 4068, 2184, 4320, 3978, 4052, 1622, 926, 3140, 231, 157, 2160, 1404, 6084, 3809, 1598, 2092, 6255, 2234, 3750, 5405, 3459, 3669, 23, 1463, 974, 2675, 2891, 2166, 712, 5030, 5023, 5080, 2741, 308, 32, 2203, 5217, 4593, 1437, 303, 2112, 3975], "prompt_text": " So I am gonna do this right now. So let's do it.", "llm_prompt_speech_token": [1822, 5727, 5000, 930, 5015, 2912, 3616, 692, 1250, 1978, 4214, 3485, 2036, 1298, 2918, 5192, 5056, 5074, 5065, 4813, 3005, 3002, 3313, 4238, 795, 4523, 4520, 3038, 4496, 859, 1887, 2490, 3309, 6235, 5264, 6074, 6047, 5339, 5474, 4291, 2915, 2666, 3759, 4056, 4299, 3975, 6159, 6186, 6186, 6186, 5838, 5109, 3732, 2112, 2139, 3945, 4534, 4569, 4575, 6453, 5405, 4461, 4338, 5572, 3809, 2411, 1214, 1205, 3805, 4526, 4379, 2189, 3890, 3242, 1418, 2876, 5828, 2799, 5133, 5563, 5481, 2325, 155, 533, 2801, 3617, 725, 56, 4385, 834, 3444, 5482, 3273, 2166, 2328, 1908, 1372, 868]}

We use Deepspeed to train the model:

deepspeed --num_nodes 1 --num_gpus 4 train_scripts/train_llm.py --data_file /external_data/yueyudata/speech_corpus/ --model_name /external_data/models/rwkv7-1.5B-world/ --output_dir /external_data/yueyudata/cosy_voice_llm --max_length 2048 --wandb_project toy_cosy_llm --wandb_run_name server2_rwkv_7_1.5B --ds_param_offload True --ds_optimizer_offload True --ds_stage 2 --gradient_checkpointing True --logging_steps 10 --per_device_train_batch_size 8

The base model can be downloaded from https://huggingface.co/collections/fla-hub/rwkv7-6790fd37b4b6137b088a0d8a , just choose a proper model for your training.

Cosy 2.0 LLM Inference

Some samples

Zero shot inference

prompt audio : prompt audio

prompt text: "今天天气挺不错的。"

tts text: "收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。"

tts audio: tts audio

TODO:

1. Drop prompt audio tokens randomly to simulate unconditional guided generation.
2. Add special control tokens in Cosy 2.0 in RWKV tokenizer and add them to generate audio tokens again:

        special_tokens = {
            'eos_token': '<|endoftext|>',
            'pad_token': '<|endoftext|>',
            'additional_special_tokens': [
                '<|im_start|>', '<|im_end|>', '<|endofprompt|>',
                '[breath]', '<strong>', '</strong>', '[noise]',
                '[laughter]', '[cough]', '[clucking]', '[accent]',
                '[quick_breath]',
                "<laughter>", "</laughter>",
                "[hissing]", "[sigh]", "[vocalized-noise]",
                "[lipsmack]", "[mn]"
            ]
        }

1. Add special control tokens like dialects in RWKV7LM and generate audio tokens for training.
2. Implement streaming generation for Cosy 2.0 in RWKV7LM.