Duplicate from zai-org/GLM-TTS

Co-authored-by: zR <ZHANGYUXUAN-zR@users.noreply.huggingface.co>

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README.md

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+---
+language:
+- zh
+- en
+tags:
+- llm
+- tts
+- zero-shot
+- voice-cloning
+- reinforcement-learning
+- flow-matching
+license: mit
+pipeline_tag: text-to-speech
+library_name: glm-tts
+---
+
+# GLM-TTS: Controllable & Emotion-Expressive Zero-shot TTS
+
+<div align="center">
+<img src=https://raw.githubusercontent.com/zai-org/GLM-TTS/refs/heads/main/assets/images/logo.svg  width="50%"/>
+</div>
+
+<p align="center">
+    <a href="https://arxiv.org/abs/2512.14291" target="_blank">📜 Paper</a>
+    &nbsp;&nbsp;|&nbsp;&nbsp;
+    <a href="https://github.com/zai-org/GLM-TTS" target="_blank">💻 GitHub Repository</a>
+    &nbsp;&nbsp;|&nbsp;&nbsp;
+    <a href="https://audio.z.ai/" target="_blank">🛠️ Audio.Z.AI</a>
+</p>
+
+## 📖 Model Introduction
+
+GLM-TTS is a high-quality text-to-speech (TTS) synthesis system based on large language models, supporting zero-shot voice cloning and streaming inference. The system adopts a two-stage architecture combining an LLM for speech token generation and a Flow Matching model for waveform synthesis.
+
+By introducing a **Multi-Reward Reinforcement Learning** framework, GLM-TTS significantly improves the expressiveness of generated speech, achieving more natural emotional control compared to traditional TTS systems.
+
+### Key Features
+
+* **Zero-shot Voice Cloning:** Clone any speaker's voice with just 3-10 seconds of prompt audio.
+* **RL-enhanced Emotion Control:** Utilizes a multi-reward reinforcement learning framework (GRPO) to optimize prosody and emotion.
+* **High-quality Synthesis:** Generates speech comparable to commercial systems with reduced Character Error Rate (CER).
+* **Phoneme-level Control:** Supports "Hybrid Phoneme + Text" input for precise pronunciation control (e.g., polyphones).
+* **Streaming Inference:** Supports real-time audio generation suitable for interactive applications.
+* **Bilingual Support:** Optimized for Chinese and English mixed text.
+
+## System Architecture
+
+GLM-TTS follows a two-stage design:
+
+1.  **Stage 1 (LLM):** A Llama-based model converts input text into speech token sequences.
+2.  **Stage 2 (Flow Matching):** A Flow model converts token sequences into high-quality mel-spectrograms, which are then turned into waveforms by a vocoder.
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/zai-org/GLM-TTS/refs/heads/main/assets/images/architecture.png" width="60%" alt="GLM-TTS Architecture">
+</div>
+
+### Reinforcement Learning Alignment
+To tackle flat emotional expression, GLM-TTS uses a **Group Relative Policy Optimization (GRPO)** algorithm with multiple reward functions (Similarity, CER, Emotion, Laughter) to align the LLM's generation strategy.
+
+## Evaluation Results
+
+Evaluated on `seed-tts-eval`. **GLM-TTS_RL** achieves the lowest Character Error Rate (CER) while maintaining high speaker similarity.
+
+| Model | CER ↓ | SIM ↑ | Open-source |
+| :--- | :---: | :---: | :---: |
+| Seed-TTS | 1.12 | **79.6** | 🔒 No |
+| CosyVoice2 | 1.38 | 75.7 | 👐 Yes |
+| F5-TTS | 1.53 | 76.0 | 👐 Yes |
+| **GLM-TTS (Base)** | 1.03 | 76.1 | 👐 Yes |
+| **GLM-TTS_RL (Ours)** | **0.89** | 76.4 | 👐 Yes |
+
+## Quick Start
+
+### Installation
+
+```bash
+git clone [https://github.com/zai-org/GLM-TTS.git](https://github.com/zai-org/GLM-TTS.git)
+cd GLM-TTS
+pip install -r requirements.txt
+```
+
+#### Command Line Inference
+
+```bash
+python glmtts_inference.py \
+    --data=example_zh \
+    --exp_name=_test \
+    --use_cache \
+    # --phoneme # Add this flag to enable phoneme capabilities.
+```
+
+#### Shell Script Inference
+
+```bash
+bash glmtts_inference.sh
+```
+
+## Acknowledgments & Citation
+
+We thank the following open-source projects for their support:
+
+- [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) - Providing frontend processing framework and high-quality vocoder
+- [Llama](https://github.com/meta-llama/llama) - Providing basic language model architecture
+- [Vocos](https://github.com/charactr-platform/vocos) - Providing high-quality vocoder
+- [GRPO-Zero](https://github.com/policy-gradient/GRPO-Zero) - Reinforcement learning algorithm implementation inspiration
+
+If you find GLM-TTS useful for your research, please cite our technical report:
+
+```bibtex
+@misc{cui2025glmttstechnicalreport,
+      title={GLM-TTS Technical Report}, 
+      author={Jiayan Cui and Zhihan Yang and Naihan Li and Jiankun Tian and Xingyu Ma and Yi Zhang and Guangyu Chen and Runxuan Yang and Yuqing Cheng and Yizhi Zhou and Guochen Yu and Xiaotao Gu and Jie Tang},
+      year={2025},
+      eprint={2512.14291},
+      archivePrefix={arXiv},
+      primaryClass={cs.SD},
+      url={https://arxiv.org/abs/2512.14291}, 
+}
+}

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+    spkr_emb_adaLN: True
+    speech_token_cfg: False
+    remove_spkr_concat_condition: True
+    mel_dim: 80
+    mel_framerate: 50
+    input_frame_rate: 25

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+  "transformers_version": "4.47.1",
+  "use_cache": true,
+  "vocab_size": 98304
+}

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speech_tokenizer/config.json

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+{
+  "_name_or_path": "/workspace/audio_team/usr/jiayan/jiayan--TTS3--1.5B/pretrained_models/whisper-large-v3",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperVQForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 1280,
+  "decoder_attention_heads": 20,
+  "decoder_ffn_dim": 5120,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 32,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "encoder_attention_heads": 20,
+  "encoder_causal_attention": false,
+  "encoder_causal_convolution": false,
+  "encoder_ffn_dim": 5120,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 32,
+  "eos_token_id": 50257,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 32,
+  "num_mel_bins": 128,
+  "pad_token_id": 50256,
+  "pitch_control": "fcpe_f0",
+  "pitch_loss_scale": 2.0,
+  "pitch_upsample_scale": 2,
+  "pooling_kernel_size": 2,
+  "pooling_position": 16,
+  "pooling_type": "avg",
+  "quantize_causal_block_size": 200,
+  "quantize_causal_encoder": false,
+  "quantize_commit_coefficient": 0.25,
+  "quantize_ema_decay": 0.99,
+  "quantize_encoder_only": false,
+  "quantize_hidden_dim": null,
+  "quantize_loss_scale": 10.0,
+  "quantize_position": 16,
+  "quantize_restart_interval": 100,
+  "quantize_vocab_size": 32768,
+  "scale_embedding": false,
+  "skip_language_detection": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.1",
+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51866
+}

speech_tokenizer/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:78e3a3542b8e111a2ecb363056c643dafdef3624eacce3af587693421a55e38e
+size 1628135704

speech_tokenizer/preprocessor_config.json

@@ -0,0 +1,14 @@
+{
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}

vocos2d/generator_jit.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:acfccc69db407008db8388eea1b8229151bdb822083211f757f5c1c73847913a
+size 60429952

vq32k-phoneme-tokenizer/tokenization_chatglm.py

@@ -0,0 +1,224 @@
+import regex as re
+import base64
+import os
+import tiktoken
+from typing import List, Optional, Union, Dict
+from transformers import PreTrainedTokenizer
+from transformers.utils import PaddingStrategy
+from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
+
+
+class ChatGLM4Tokenizer(PreTrainedTokenizer):
+    vocab_files_names = {"vocab_file": "tokenizer.model"}
+    model_input_names = ["input_ids", "attention_mask", "position_ids"]
+
+    def __init__(
+            self,
+            vocab_file,
+            clean_up_tokenization_spaces=False,
+            **kwargs
+    ):
+        self.name = "GLM4Tokenizer"
+        self.vocab_file = vocab_file
+        pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+        self.pat_str = re.compile(pat_str)
+
+        mergeable_ranks = {}
+        with open(vocab_file) as f:
+            for line in f:
+                token, rank = line.strip().split()
+                rank = int(rank)
+                token = base64.b64decode(token)
+                mergeable_ranks[token] = rank
+
+        self.mergeable_ranks = mergeable_ranks
+
+        self.tokenizer = tiktoken.Encoding(
+            name="my_tokenizer",
+            pat_str=pat_str,
+            mergeable_ranks=mergeable_ranks,
+            special_tokens={}
+        )
+        self.decoder = {rank: token for token, rank in mergeable_ranks.items()}
+        self.n_words = len(self.decoder)
+
+        super().__init__(
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs
+        )
+
+    @property
+    def vocab_size(self):
+        return self.n_words
+
+    def get_vocab(self):
+        """ Returns vocab as a dict """
+        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def convert_tokens_to_string(self, tokens: List[Union[bytes, str, int]]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        text = ""
+        temp = b""
+        for t in tokens:
+            if isinstance(t, int):
+                t = chr(t)
+            if isinstance(t, str):
+                if temp:
+                    text += temp.decode("utf-8", errors="replace")
+            elif isinstance(t, bytes):
+                temp += t
+            else:
+                raise TypeError("token should only be of type int, bytes or str")
+        if temp:
+            text += temp.decode("utf-8", errors="replace")
+        return text
+
+    def _tokenize(self, text, **kwargs):
+        tokens = []
+        ids = self.tokenizer.encode(text)
+        for t in ids:
+            tokens.append(self.decoder[t])
+        return tokens
+
+    def _convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        return self.mergeable_ranks[token]
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.decoder.get(index, "")
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+            filename_prefix (`str`, *optional*):
+                An optional prefix to add to the named of the saved files.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, self.vocab_files_names["vocab_file"]
+            )
+        else:
+            vocab_file = save_directory
+
+        with open(self.vocab_file, 'rb') as fin:
+            proto_str = fin.read()
+
+        with open(vocab_file, "wb") as writer:
+            writer.write(proto_str)
+
+        return (vocab_file,)
+
+    def get_prefix_tokens(self):
+        prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")]
+        return prefix_tokens
+
+    def build_single_message(self, role, metadata, message, tokenize=True):
+        assert role in ["system", "user", "assistant", "observation"], role
+        if tokenize:
+            role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n",
+                                                                                              disallowed_special=())
+            message_tokens = self.tokenizer.encode(message, disallowed_special=())
+            tokens = role_tokens + message_tokens
+            return tokens
+        else:
+            return str(f"<|{role}|>{metadata}\n{message}")
+
+    def build_inputs_with_special_tokens(
+            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A BERT sequence has the following format:
+
+        - single sequence: `[CLS] X [SEP]`
+        - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        # prefix_tokens = self.get_prefix_tokens()
+        # token_ids_0 = prefix_tokens + token_ids_0
+        if token_ids_1 is not None:
+            token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")]
+        return token_ids_0
+
+    def _pad(
+            self,
+            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
+            max_length: Optional[int] = None,
+            padding_side: str = "left",
+            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+            pad_to_multiple_of: Optional[int] = None,
+            return_attention_mask: Optional[bool] = None,
+    ) -> dict:
+        """
+        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+        Args:
+            encoded_inputs:
+                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+            max_length: maximum length of the returned list and optionally padding length (see below).
+                Will truncate by taking into account the special tokens.
+            padding_strategy: PaddingStrategy to use for padding.
+
+                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+                - PaddingStrategy.DO_NOT_PAD: Do not pad
+                The tokenizer padding sides are defined in self.padding_side:
+
+                    - 'left': pads on the left of the sequences
+                    - 'right': pads on the right of the sequences
+            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+                `>= 7.5` (Volta).
+            return_attention_mask:
+                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+        """
+        # Load from model defaults
+
+        required_input = encoded_inputs[self.model_input_names[0]]
+        seq_length = len(required_input)
+
+        if padding_strategy == PaddingStrategy.LONGEST:
+            max_length = len(required_input)
+
+        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+        # Initialize attention mask if not present.
+        if "attention_mask" not in encoded_inputs:
+            encoded_inputs["attention_mask"] = [1] * seq_length
+
+        if "position_ids" not in encoded_inputs:
+            encoded_inputs["position_ids"] = list(range(seq_length))
+
+        if needs_to_be_padded:
+            difference = max_length - len(required_input)
+
+            if "attention_mask" in encoded_inputs:
+                encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+            if "position_ids" in encoded_inputs:
+                encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
+            encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+
+        return encoded_inputs

vq32k-phoneme-tokenizer/tokenizer.model

@@ -0,0 +1,3 @@
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+size 1044856