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url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload-time = "2025-06-08T17:06:38.034Z" }, +] From 429d73ee0af7d39886ddeb0f4e43d2472e8346d1 Mon Sep 17 00:00:00 2001 From: Lukin Date: Mon, 9 Jun 2025 07:41:55 +0000 Subject: [PATCH 02/10] Add pause tag parsing and silence generation in TTS --- src/chatterbox/tts.py | 97 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 96 insertions(+), 1 deletion(-) diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index 6d9b5ad5..74072fee 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -7,6 +7,8 @@ import torch.nn.functional as F from huggingface_hub import hf_hub_download from safetensors.torch import load_file +import re +import numpy as np from .models.t3 import T3 from .models.s3tokenizer import S3_SR, drop_invalid_tokens @@ -230,6 +232,39 @@ def generate( emotion_adv=exaggeration * torch.ones(1, 1, 1), ).to(device=self.device) + # Parse pause tags BEFORE applying punc_norm to preserve the tags + segments = parse_pause_tags(text) + + # If no pause tags, use original logic + if len(segments) == 1 and segments[0][1] == 0.0: + return self._generate_single_segment( + segments[0][0], cfg_weight, temperature + ) + + # Process text with pauses + audio_segments = [] + + for text_segment, pause_duration in segments: + if text_segment.strip(): # Non-empty text segment + segment_audio = self._generate_single_segment( + text_segment, cfg_weight, temperature + ) + audio_segments.append(segment_audio.squeeze(0)) + + if pause_duration > 0: # Add silence + silence = create_silence(pause_duration, self.sr) + audio_segments.append(silence.squeeze(0)) + + # Concatenate all audio segments + if audio_segments: + final_audio = torch.cat(audio_segments, dim=0) + return final_audio.unsqueeze(0) + else: + # If no valid audio segments, return brief silence + return create_silence(0.1, self.sr) + + def _generate_single_segment(self, text, cfg_weight, temperature): + """Generate audio for a single text segment""" # Norm and tokenize text text = punc_norm(text) text_tokens = self.tokenizer.text_to_tokens(text).to(self.device) @@ -269,4 +304,64 @@ def generate( ) wav = wav.squeeze(0).detach().cpu().numpy() watermarked_wav = self.watermarker.apply_watermark(wav, sample_rate=self.sr) - return torch.from_numpy(watermarked_wav).unsqueeze(0) \ No newline at end of file + return torch.from_numpy(watermarked_wav).unsqueeze(0) + +def parse_pause_tags(text: str): + """ + Parse pause tags in text and return text segments with corresponding pause durations + + Args: + text: Text containing pause tags like "Hello[pause:0.5s]world[pause:1.0s]end" + + Returns: + segments: [(text_segment, pause_duration), ...] + Example: [("Hello", 0.5), ("world", 1.0), ("end", 0.0)] + """ + if not text: + return [("", 0.0)] + + # Regular expression to match pause tags + pause_pattern = r'\[pause:([\d.]+)s\]' + + segments = [] + last_end = 0 + + # Find all pause tags + for match in re.finditer(pause_pattern, text): + # Extract text before the pause tag + text_segment = text[last_end:match.start()].strip() + if text_segment: + segments.append((text_segment, 0.0)) + + # Extract pause duration + pause_duration = float(match.group(1)) + # Ensure pause duration is a multiple of 0.1s + pause_duration = round(pause_duration / 0.1) * 0.1 + segments.append(("", pause_duration)) + + last_end = match.end() + + # Add the final text segment + final_text = text[last_end:].strip() + if final_text: + segments.append((final_text, 0.0)) + + # If no segments found, return original text + if not segments: + segments = [(text, 0.0)] + + return segments + +def create_silence(duration_seconds: float, sample_rate: int) -> torch.Tensor: + """ + Create silence of specified duration + + Args: + duration_seconds: Duration of silence in seconds + sample_rate: Sample rate + + Returns: + Silent audio tensor + """ + num_samples = int(duration_seconds * sample_rate) + return torch.zeros(1, num_samples) \ No newline at end of file From 0c0fdfae3e080461b9e0cbf3213953bfebe2f245 Mon Sep 17 00:00:00 2001 From: Lukin Date: Sun, 15 Jun 2025 03:31:15 +0000 Subject: [PATCH 03/10] Update Python requirement to 3.10, enhance TTS with auto-editor for artifact cleaning, and add support for custom pause tags in audio generation. --- Chatterbox-TTS-Extended | 1 + README.md | 11 + pyproject.toml | 3 +- src/chatterbox/tts.py | 202 +++++- uv.lock | 1317 ++++++--------------------------------- 5 files changed, 373 insertions(+), 1161 deletions(-) create mode 160000 Chatterbox-TTS-Extended diff --git a/Chatterbox-TTS-Extended b/Chatterbox-TTS-Extended new file mode 160000 index 00000000..d71916a9 --- /dev/null +++ b/Chatterbox-TTS-Extended @@ -0,0 +1 @@ +Subproject commit d71916a9a0a8c0d72fc48436a461e2894fe06cca diff --git a/README.md b/README.md index d6651ef7..b3d1cc62 100644 --- a/README.md +++ b/README.md @@ -26,6 +26,8 @@ If you like the model but need to scale or tune it for higher accuracy, check ou - Watermarked outputs - Easy voice conversion script - [Outperforms ElevenLabs](https://podonos.com/resembleai/chatterbox) +- Smart artifact cleaning with pause protection +- Support for custom pause tags `[pause:xx]` # Supported Languages Arabic (ar) • Danish (da) • German (de) • Greek (el) • English (en) • Spanish (es) • Finnish (fi) • French (fr) • Hebrew (he) • Hindi (hi) • Italian (it) • Japanese (ja) • Korean (ko) • Malay (ms) • Dutch (nl) • Norwegian (no) • Polish (pl) • Portuguese (pt) • Russian (ru) • Swedish (sv) • Swahili (sw) • Turkish (tr) • Chinese (zh) @@ -84,6 +86,15 @@ ta.save("test-chinese.wav", wav_chinese, model.sr) AUDIO_PROMPT_PATH = "YOUR_FILE.wav" wav = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH) ta.save("test-2.wav", wav, model.sr) + +# 使用伪影清理功能 +wav = model.generate( + text="Hello[pause:0.5s]world!", # 支持[pause:xx]标签添加停顿 + use_auto_editor=True, # 启用伪影清理 + ae_threshold=0.06, # 音量阈值(0-1) + ae_margin=0.2 # 边界保护时间(秒) +) +ta.save("test-3.wav", wav, model.sr) ``` See `example_tts.py` and `example_vc.py` for more examples. diff --git a/pyproject.toml b/pyproject.toml index a70d61e8..5be8a6fc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,6 +10,7 @@ authors = [ ] dependencies = [ "numpy>=1.24.0,<1.26.0", + "resampy==0.4.3", "librosa==0.11.0", "s3tokenizer", "torch==2.6.0", @@ -23,7 +24,7 @@ dependencies = [ "pykakasi==2.3.0", "gradio==5.44.1", "russian-text-stresser @ git+https://github.com/Vuizur/add-stress-to-epub", - + "auto-editor>=27.0.0" ] [project.urls] diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index 74072fee..d9f9dada 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -1,5 +1,8 @@ from dataclasses import dataclass from pathlib import Path +import tempfile +import subprocess +import os import librosa import torch @@ -9,6 +12,7 @@ from safetensors.torch import load_file import re import numpy as np +import torchaudio from .models.t3 import T3 from .models.s3tokenizer import S3_SR, drop_invalid_tokens @@ -217,6 +221,10 @@ def generate( exaggeration=0.5, cfg_weight=0.5, temperature=0.8, + use_auto_editor=False, + ae_threshold=0.06, + ae_margin=0.2, + disable_watermark=False, ): if audio_prompt_path: self.prepare_conditionals(audio_prompt_path, exaggeration=exaggeration) @@ -235,35 +243,108 @@ def generate( # Parse pause tags BEFORE applying punc_norm to preserve the tags segments = parse_pause_tags(text) - # If no pause tags, use original logic - if len(segments) == 1 and segments[0][1] == 0.0: - return self._generate_single_segment( - segments[0][0], cfg_weight, temperature + # Single segment processing (simplified logic for single text without pauses) + if len(segments) == 1 and segments[0][1] == 0.0: # Single text, no pause + text_segment = segments[0][0] + segment_audio = self._generate_single_segment( + text_segment, cfg_weight, temperature, disable_watermark ) + + # 清理伪影(如果启用) + if use_auto_editor: + # 保存临时音频文件 + import tempfile + with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_file: + temp_audio_path = temp_file.name + torchaudio.save(temp_audio_path, segment_audio, self.sr) + + # 清理伪影 + cleaned_audio_path = self._clean_artifacts(temp_audio_path, ae_threshold, ae_margin) + + # 加载清理后的音频 + if cleaned_audio_path != temp_audio_path: + try: + cleaned_audio, _ = torchaudio.load(cleaned_audio_path) + # 清理临时文件 + if os.path.exists(temp_audio_path): + os.unlink(temp_audio_path) + if os.path.exists(cleaned_audio_path): + os.unlink(cleaned_audio_path) + return cleaned_audio + except Exception as e: + print(f"[WARNING] 无法加载清理后的音频: {e}") + # 清理临时文件 + if os.path.exists(temp_audio_path): + os.unlink(temp_audio_path) + if os.path.exists(cleaned_audio_path): + os.unlink(cleaned_audio_path) + else: + # 清理失败，使用原始音频 + if os.path.exists(temp_audio_path): + os.unlink(temp_audio_path) + + return segment_audio - # Process text with pauses + # Process text with pauses - 先生成并清理每段，再添加停顿 audio_segments = [] + temp_files_to_cleanup = [] - for text_segment, pause_duration in segments: - if text_segment.strip(): # Non-empty text segment - segment_audio = self._generate_single_segment( - text_segment, cfg_weight, temperature - ) - audio_segments.append(segment_audio.squeeze(0)) + try: + for text_segment, pause_duration in segments: + if text_segment.strip(): # Non-empty text segment + # 1. 生成音频段 + segment_audio = self._generate_single_segment( + text_segment, cfg_weight, temperature, disable_watermark + ) + + # 2. 清理该段的伪影 + if use_auto_editor: + # 保存临时音频文件 + import tempfile + with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_file: + temp_audio_path = temp_file.name + temp_files_to_cleanup.append(temp_audio_path) + torchaudio.save(temp_audio_path, segment_audio, self.sr) + + # 清理伪影 + cleaned_audio_path = self._clean_artifacts(temp_audio_path, ae_threshold, ae_margin) + if cleaned_audio_path != temp_audio_path: + temp_files_to_cleanup.append(cleaned_audio_path) + + # 加载清理后的音频 + try: + if cleaned_audio_path != temp_audio_path: + cleaned_audio, _ = torchaudio.load(cleaned_audio_path) + segment_audio = cleaned_audio + except Exception as e: + print(f"[WARNING] 无法加载清理后的音频段: {e}") + # 继续使用原始音频段 + + audio_segments.append(segment_audio.squeeze(0)) + + # 3. 添加停顿(在伪影清理之后) + if pause_duration > 0: + silence = create_silence(pause_duration, self.sr) + audio_segments.append(silence.squeeze(0)) - if pause_duration > 0: # Add silence - silence = create_silence(pause_duration, self.sr) - audio_segments.append(silence.squeeze(0)) - - # Concatenate all audio segments - if audio_segments: - final_audio = torch.cat(audio_segments, dim=0) - return final_audio.unsqueeze(0) - else: - # If no valid audio segments, return brief silence - return create_silence(0.1, self.sr) - - def _generate_single_segment(self, text, cfg_weight, temperature): + # 4. 拼接所有音频段 + if audio_segments: + final_audio = torch.cat(audio_segments, dim=0) + return final_audio.unsqueeze(0) + else: + # If no valid audio segments, return brief silence + return create_silence(0.1, self.sr) + + finally: + # 清理所有临时文件 + for temp_file in temp_files_to_cleanup: + if os.path.exists(temp_file): + try: + os.unlink(temp_file) + except: + pass # 忽略清理错误 + + def _generate_single_segment(self, text, cfg_weight, temperature, disable_watermark=False): """Generate audio for a single text segment""" # Norm and tokenize text text = punc_norm(text) @@ -303,8 +384,76 @@ def _generate_single_segment(self, text, cfg_weight, temperature): ref_dict=self.conds.gen, ) wav = wav.squeeze(0).detach().cpu().numpy() - watermarked_wav = self.watermarker.apply_watermark(wav, sample_rate=self.sr) - return torch.from_numpy(watermarked_wav).unsqueeze(0) + + if not disable_watermark: + watermarked_wav = self.watermarker.apply_watermark(wav, sample_rate=self.sr) + return torch.from_numpy(watermarked_wav).unsqueeze(0) + else: + return torch.from_numpy(wav).unsqueeze(0) + + def _clean_artifacts(self, audio_path: str, threshold: float = 0.06, margin: float = 0.2) -> str: + """ + 使用 auto-editor 清理音频伪影 + + Args: + audio_path: 输入音频文件路径 + threshold: 音量阈值, 低于此值认为是静音/伪影 + margin: 边界保护时间(秒) + + Returns: + 清理后的音频文件路径 + """ + import subprocess + import tempfile + import os + + # 创建输出文件 + output_file = tempfile.NamedTemporaryFile(suffix='_cleaned.wav', delete=False) + output_file.close() + + try: + # 构建 auto-editor 命令(适配 28.0.0 版本) + cmd = [ + "auto-editor", + audio_path, + "--edit", f"audio:threshold={threshold}", + "--margin", f"{margin}s", + "--output-file", output_file.name + ] + + print(f"[INFO] 清理伪影: {' '.join(cmd)}") + + # 执行 auto-editor + result = subprocess.run( + cmd, + capture_output=True, + text=True, + timeout=60, + check=True + ) + + if os.path.exists(output_file.name) and os.path.getsize(output_file.name) > 0: + print(f"[INFO] 伪影清理完成: {output_file.name}") + return output_file.name + else: + raise RuntimeError("auto-editor 没有生成有效的输出文件") + + except subprocess.CalledProcessError as e: + print(f"[ERROR] auto-editor 执行失败: {e}") + print(f"[ERROR] stderr: {e.stderr}") + print(f"[ERROR] stdout: {e.stdout}") + # 清理失败的输出文件 + if os.path.exists(output_file.name): + os.unlink(output_file.name) + return audio_path # 返回原始文件 + + except Exception as e: + print(f"[ERROR] 伪影清理过程中出现异常: {e}") + # 清理失败的输出文件 + if os.path.exists(output_file.name): + os.unlink(output_file.name) + return audio_path # 返回原始文件 + def parse_pause_tags(text: str): """ @@ -352,6 +501,7 @@ def parse_pause_tags(text: str): return segments + def create_silence(duration_seconds: float, sample_rate: int) -> torch.Tensor: """ Create silence of specified duration diff --git a/uv.lock b/uv.lock index 77870bd0..0221f585 100644 --- a/uv.lock +++ b/uv.lock @@ -1,36 +1,17 @@ version = 1 revision = 2 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6bc32e02b6f07855b788b682469069638f1c2e96 Mon Sep 17 00:00:00 2001 From: Lukin Date: Sun, 15 Jun 2025 03:32:15 +0000 Subject: [PATCH 04/10] Remove Chatterbox-TTS-Extended subproject, cleaning up the repository. --- Chatterbox-TTS-Extended | 1 - 1 file changed, 1 deletion(-) delete mode 160000 Chatterbox-TTS-Extended diff --git a/Chatterbox-TTS-Extended b/Chatterbox-TTS-Extended deleted file mode 160000 index d71916a9..00000000 --- a/Chatterbox-TTS-Extended +++ /dev/null @@ -1 +0,0 @@ -Subproject commit d71916a9a0a8c0d72fc48436a461e2894fe06cca From 7c93ca33850dc2ae77cfb2ff6313345511532ad1 Mon Sep 17 00:00:00 2001 From: Lukin Date: Sun, 15 Jun 2025 03:43:16 +0000 Subject: [PATCH 05/10] Update README and TTS code for artifact cleaning feature; translate comments to English and remove unused uv.lock file. --- README.md | 10 +- src/chatterbox/tts.py | 74 +- uv.lock | 2418 ----------------------------------------- 3 files changed, 42 insertions(+), 2460 deletions(-) delete mode 100644 uv.lock diff --git a/README.md b/README.md index b3d1cc62..82f9f991 100644 --- a/README.md +++ b/README.md @@ -87,12 +87,12 @@ AUDIO_PROMPT_PATH = "YOUR_FILE.wav" wav = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH) ta.save("test-2.wav", wav, model.sr) -# 使用伪影清理功能 +# Using artifact cleaning feature wav = model.generate( - text="Hello[pause:0.5s]world!", # 支持[pause:xx]标签添加停顿 - use_auto_editor=True, # 启用伪影清理 - ae_threshold=0.06, # 音量阈值(0-1) - ae_margin=0.2 # 边界保护时间(秒) + text="Hello[pause:0.5s]world!", # Support [pause:xx] tag for adding pauses + use_auto_editor=True, # Enable artifact cleaning + ae_threshold=0.06, # Volume threshold (0-1) + ae_margin=0.2 # Boundary protection time (seconds) ) ta.save("test-3.wav", wav, model.sr) ``` diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index d9f9dada..76250321 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -250,84 +250,84 @@ def generate( text_segment, cfg_weight, temperature, disable_watermark ) - # 清理伪影(如果启用) + # Clean artifacts (if enabled) if use_auto_editor: - # 保存临时音频文件 + # Save temporary audio file import tempfile with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_file: temp_audio_path = temp_file.name torchaudio.save(temp_audio_path, segment_audio, self.sr) - # 清理伪影 + # Clean artifacts cleaned_audio_path = self._clean_artifacts(temp_audio_path, ae_threshold, ae_margin) - # 加载清理后的音频 + # Load cleaned audio if cleaned_audio_path != temp_audio_path: try: cleaned_audio, _ = torchaudio.load(cleaned_audio_path) - # 清理临时文件 + # Clean temporary files if os.path.exists(temp_audio_path): os.unlink(temp_audio_path) if os.path.exists(cleaned_audio_path): os.unlink(cleaned_audio_path) return cleaned_audio except Exception as e: - print(f"[WARNING] 无法加载清理后的音频: {e}") - # 清理临时文件 + print(f"[WARNING] Unable to load cleaned audio: {e}") + # Clean temporary files if os.path.exists(temp_audio_path): os.unlink(temp_audio_path) if os.path.exists(cleaned_audio_path): os.unlink(cleaned_audio_path) else: - # 清理失败，使用原始音频 + # Cleaning failed, use original audio if os.path.exists(temp_audio_path): os.unlink(temp_audio_path) return segment_audio - # Process text with pauses - 先生成并清理每段，再添加停顿 + # Process text with pauses - generate and clean each segment first, then add pauses audio_segments = [] temp_files_to_cleanup = [] try: for text_segment, pause_duration in segments: if text_segment.strip(): # Non-empty text segment - # 1. 生成音频段 + # 1. Generate audio segment segment_audio = self._generate_single_segment( text_segment, cfg_weight, temperature, disable_watermark ) - # 2. 清理该段的伪影 + # 2. Clean artifacts for this segment if use_auto_editor: - # 保存临时音频文件 + # Save temporary audio file import tempfile with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_file: temp_audio_path = temp_file.name temp_files_to_cleanup.append(temp_audio_path) torchaudio.save(temp_audio_path, segment_audio, self.sr) - # 清理伪影 + # Clean artifacts cleaned_audio_path = self._clean_artifacts(temp_audio_path, ae_threshold, ae_margin) if cleaned_audio_path != temp_audio_path: temp_files_to_cleanup.append(cleaned_audio_path) - # 加载清理后的音频 + # Load cleaned audio try: if cleaned_audio_path != temp_audio_path: cleaned_audio, _ = torchaudio.load(cleaned_audio_path) segment_audio = cleaned_audio except Exception as e: - print(f"[WARNING] 无法加载清理后的音频段: {e}") - # 继续使用原始音频段 + print(f"[WARNING] Unable to load cleaned audio segment: {e}") + # Continue using original audio segment audio_segments.append(segment_audio.squeeze(0)) - # 3. 添加停顿(在伪影清理之后) + # 3. Add pause (after artifact cleaning) if pause_duration > 0: silence = create_silence(pause_duration, self.sr) audio_segments.append(silence.squeeze(0)) - # 4. 拼接所有音频段 + # 4. Concatenate all audio segments if audio_segments: final_audio = torch.cat(audio_segments, dim=0) return final_audio.unsqueeze(0) @@ -336,13 +336,13 @@ def generate( return create_silence(0.1, self.sr) finally: - # 清理所有临时文件 + # Clean up all temporary files for temp_file in temp_files_to_cleanup: if os.path.exists(temp_file): try: os.unlink(temp_file) except: - pass # 忽略清理错误 + pass # Ignore cleanup errors def _generate_single_segment(self, text, cfg_weight, temperature, disable_watermark=False): """Generate audio for a single text segment""" @@ -393,26 +393,26 @@ def _generate_single_segment(self, text, cfg_weight, temperature, disable_waterm def _clean_artifacts(self, audio_path: str, threshold: float = 0.06, margin: float = 0.2) -> str: """ - 使用 auto-editor 清理音频伪影 + Clean artifacts from audio using auto-editor Args: - audio_path: 输入音频文件路径 - threshold: 音量阈值, 低于此值认为是静音/伪影 - margin: 边界保护时间(秒) + audio_path: Path to input audio file + threshold: Volume threshold, values below this are considered silence/artifacts + margin: Boundary protection time in seconds Returns: - 清理后的音频文件路径 + Path to cleaned audio file """ import subprocess import tempfile import os - # 创建输出文件 + # Create output file output_file = tempfile.NamedTemporaryFile(suffix='_cleaned.wav', delete=False) output_file.close() try: - # 构建 auto-editor 命令(适配 28.0.0 版本) + # Build auto-editor command (adapted for 28.0.0 version) cmd = [ "auto-editor", audio_path, @@ -421,9 +421,9 @@ def _clean_artifacts(self, audio_path: str, threshold: float = 0.06, margin: flo "--output-file", output_file.name ] - print(f"[INFO] 清理伪影: {' '.join(cmd)}") + print(f"[INFO] Cleaning artifacts: {' '.join(cmd)}") - # 执行 auto-editor + # Execute auto-editor result = subprocess.run( cmd, capture_output=True, @@ -433,26 +433,26 @@ def _clean_artifacts(self, audio_path: str, threshold: float = 0.06, margin: flo ) if os.path.exists(output_file.name) and os.path.getsize(output_file.name) > 0: - print(f"[INFO] 伪影清理完成: {output_file.name}") + print(f"[INFO] Artifact cleaning completed: {output_file.name}") return output_file.name else: - 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{ url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload-time = "2025-06-08T17:06:38.034Z" }, -] From 772a5f1b24a21623e99be07fbe2780082d2a807e Mon Sep 17 00:00:00 2001 From: Lukin Date: Sun, 15 Jun 2025 03:52:18 +0000 Subject: [PATCH 06/10] Enhance TTS audio generation by adding repetition_penalty, min_p, and top_p parameters to _generate_single_segment method and its calls, improving flexibility in audio output configuration. --- src/chatterbox/tts.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index 76250321..dfc879c4 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -247,7 +247,7 @@ def generate( if len(segments) == 1 and segments[0][1] == 0.0: # Single text, no pause text_segment = segments[0][0] segment_audio = self._generate_single_segment( - text_segment, cfg_weight, temperature, disable_watermark + text_segment, cfg_weight, temperature, repetition_penalty, min_p, top_p, disable_watermark ) # Clean artifacts (if enabled) @@ -294,7 +294,7 @@ def generate( if text_segment.strip(): # Non-empty text segment # 1. Generate audio segment segment_audio = self._generate_single_segment( - text_segment, cfg_weight, temperature, disable_watermark + text_segment, cfg_weight, temperature, repetition_penalty, min_p, top_p, disable_watermark ) # 2. Clean artifacts for this segment @@ -344,7 +344,7 @@ def generate( except: pass # Ignore cleanup errors - def _generate_single_segment(self, text, cfg_weight, temperature, disable_watermark=False): + def _generate_single_segment(self, text, cfg_weight, temperature, repetition_penalty=1.2, min_p=0.05, top_p=1.0, disable_watermark=False): """Generate audio for a single text segment""" # Norm and tokenize text text = punc_norm(text) From b3f6e1f8e99587c22c8f86338ea00b3afcdeb147 Mon Sep 17 00:00:00 2001 From: Lukin Date: Thu, 19 Jun 2025 05:24:07 +0000 Subject: [PATCH 07/10] Add asynchronous long text audio generation in ChatterboxTTS This update introduces a new method for handling long text inputs by splitting them into segments and generating audio asynchronously. It includes enhancements for managing pause tags and cleaning audio segments, improving overall performance and flexibility in audio generation. --- src/chatterbox/__init__.py | 3 +- src/chatterbox/text_utils.py | 178 +++ src/chatterbox/tts.py | 185 +++ uv.lock | 2393 ++++++++++++++++++++++++++++++++++ 4 files changed, 2758 insertions(+), 1 deletion(-) create mode 100644 src/chatterbox/text_utils.py create mode 100644 uv.lock diff --git a/src/chatterbox/__init__.py b/src/chatterbox/__init__.py index 190cfbf2..a0fc1513 100644 --- a/src/chatterbox/__init__.py +++ b/src/chatterbox/__init__.py @@ -8,4 +8,5 @@ from .tts import ChatterboxTTS from .vc import ChatterboxVC -from .mtl_tts import ChatterboxMultilingualTTS, SUPPORTED_LANGUAGES \ No newline at end of file +from .mtl_tts import ChatterboxMultilingualTTS, SUPPORTED_LANGUAGES +from .text_utils import split_text_into_segments, split_by_word_boundary, merge_short_sentences diff --git a/src/chatterbox/text_utils.py b/src/chatterbox/text_utils.py new file mode 100644 index 00000000..4dba8234 --- /dev/null +++ b/src/chatterbox/text_utils.py @@ -0,0 +1,178 @@ +""" +文本处理工具函数 + +包含长文本拆分、句子合并等文本处理功能 +""" + +import re +from typing import List, Optional +import logging + + +def split_by_word_boundary(text: str, max_len: int) -> List[str]: + """ + 按单词边界拆分文本，确保不会在单词中间断开 + + 参数: + text: 要拆分的文本 + max_len: 每个分段的最大长度 + + 返回: + 文本分段列表 + """ + # 首先尝试在标点符号处分割 + punct_pattern = r'(?<=[.!?,;:])\s+' + punct_splits = re.split(punct_pattern, text) + + segments = [] + for split in punct_splits: + if len(split) <= max_len: + segments.append(split) + continue + + # 如果标点分割后的片段仍然过长，按单词边界拆分 + words = split.split() + current = "" + + for word in words: + # 如果单词本身超过最大长度，尝试保留完整单词 + if len(word) > max_len: + if current: + segments.append(current) + segments.append(word) + current = "" + continue + + # 检查添加这个单词是否会超过最大长度 + potential_segment = (current + " " + word).strip() if current else word + if len(potential_segment) > max_len: + if current: + segments.append(current) + current = word + else: + current = potential_segment + + # 添加最后剩余的部分 + if current: + segments.append(current) + + return segments + + +def merge_short_sentences(sentences: List[str], max_length: int, min_length: int = 20) -> List[str]: + """ + 合并短句子到下一句，确保不超过最大长度限制 + + 参数: + sentences: 句子列表 + max_length: 合并后句子的最大长度限制 + min_length: 短句子的长度阈值 + + 返回: + 合并后的句子列表 + """ + if not sentences: + return [] + + result = [] + i = 0 + + while i < len(sentences): + current = sentences[i].strip() + + # 跳过空句子 + if not current: + i += 1 + continue + + # 如果当前句子长度大于等于min_length，直接添加到结果中 + if len(current) >= min_length: + result.append(current) + i += 1 + continue + + # 当前句子是短句，尝试与后面的句子合并 + merged = current + j = i + 1 + while j < len(sentences) and len(merged) < min_length: + next_sentence = sentences[j].strip() + if not next_sentence: + j += 1 + continue + + # 检查合并后是否超过最大长度 + potential_merge = merged + " " + next_sentence + if len(potential_merge) <= max_length: + merged = potential_merge + j += 1 + else: + # 如果合并会超过最大长度，停止合并 + break + + # 添加合并后的结果 + if merged: + result.append(merged) + + # 更新索引 + i = j if j > i else i + 1 + + return result + + +def split_text_into_segments(text: str, max_length: int = 300, logger: Optional[logging.Logger] = None) -> List[str]: + """ + 将文本拆分成适合TTS处理的段落 + + 参数: + text: 要拆分的文本 + max_length: 每个段落的最大字符数 + logger: 可选的日志记录器 + + 返回: + 拆分后的段落列表 + """ + if not text or not text.strip(): + return [] + + text = text.strip() + + # 如果文本长度不超过最大长度，直接返回 + if len(text) <= max_length: + return [text] + + if logger: + logger.debug(f"文本长度 {len(text)} 超过最大限制 {max_length}，开始拆分") + + # 首先按段落分割（双换行符） + paragraphs = [p.strip() for p in re.split(r'\n\s*\n', text) if p.strip()] + + segments = [] + for paragraph in paragraphs: + if len(paragraph) <= max_length: + segments.append(paragraph) + continue + + # 段落过长，进一步拆分 + # 1. 尝试按句子分割（句号、问号、感叹号） + sentences = re.split(r'(?<=[.!?])\s+', paragraph) + sentences = [s.strip() for s in sentences if s.strip()] + + # 2. 合并短句子，避免产生太多碎片 + sentences = merge_short_sentences(sentences, max_length, min_length=30) + + # 3. 处理仍然过长的句子 + for sentence in sentences: + if len(sentence) <= max_length: + segments.append(sentence) + else: + # 使用单词边界拆分 + word_segments = split_by_word_boundary(sentence, max_length) + segments.extend(word_segments) + + # 清理空段落 + segments = [s.strip() for s in segments if s.strip()] + + if logger: + logger.debug(f"文本拆分完成，共 {len(segments)} 个段落") + + return segments \ No newline at end of file diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index dfc879c4..e974693b 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -3,6 +3,10 @@ import tempfile import subprocess import os +import threading +import queue +from concurrent.futures import ThreadPoolExecutor, as_completed + import librosa import torch @@ -20,6 +24,7 @@ from .models.tokenizers import EnTokenizer from .models.voice_encoder import VoiceEncoder from .models.t3.modules.cond_enc import T3Cond +from .text_utils import split_text_into_segments REPO_ID = "ResembleAI/chatterbox" @@ -225,6 +230,8 @@ def generate( ae_threshold=0.06, ae_margin=0.2, disable_watermark=False, + max_segment_length=300, + max_workers=None, # None表示自动检测最优值 ): if audio_prompt_path: self.prepare_conditionals(audio_prompt_path, exaggeration=exaggeration) @@ -240,6 +247,26 @@ def generate( emotion_adv=exaggeration * torch.ones(1, 1, 1), ).to(device=self.device) + # 长文本处理：根据文本长度自动判断是否需要拆分处理 + if len(text) > max_segment_length: + # 使用异步批量生成 + if max_workers is None: + max_workers = 3 # 简单默认值 + return self._generate_long_text_async( + text, + max_segment_length=max_segment_length, + cfg_weight=cfg_weight, + temperature=temperature, + repetition_penalty=repetition_penalty, + min_p=min_p, + top_p=top_p, + use_auto_editor=use_auto_editor, + ae_threshold=ae_threshold, + ae_margin=ae_margin, + disable_watermark=disable_watermark, + max_workers=max_workers + ) + # Parse pause tags BEFORE applying punc_norm to preserve the tags segments = parse_pause_tags(text) @@ -344,6 +371,164 @@ def generate( except: pass # Ignore cleanup errors + def _generate_long_text_async( + self, + text, + max_segment_length=300, + cfg_weight=0.5, + temperature=0.8, + repetition_penalty=1.2, + min_p=0.05, + top_p=1.0, + use_auto_editor=False, + ae_threshold=0.06, + ae_margin=0.2, + disable_watermark=False, + max_workers=3 + ): + """ + 异步生成长文本音频 - 核心优化只保留异步批量生成 + """ + # 拆分文本为短段落 + text_segments = split_text_into_segments(text, max_segment_length) + + if not text_segments: + return create_silence(0.1, self.sr) + + # 预处理文本片段，保留暂停标签信息 + all_segments = [] # [(text, pause_duration), ...] + for segment_text in text_segments: + # 处理pause tags，保留暂停信息 + segments = parse_pause_tags(segment_text) + all_segments.extend(segments) + + # 分离文本和暂停信息 + text_parts = [] + pause_info = [] + for text_part, pause_duration in all_segments: + if text_part.strip(): + text_parts.append(text_part.strip()) + pause_info.append(pause_duration) + + # 核心：异步批量生成 + audio_segments = self._generate_segments_async( + text_parts, cfg_weight, temperature, + repetition_penalty, min_p, top_p, disable_watermark, max_workers + ) + + # 应用音频清理（如果启用） + if use_auto_editor: + audio_segments = self._clean_audio_segments_batch( + audio_segments, ae_threshold, ae_margin + ) + + # 合并音频，包括暂停 + final_audio_parts = [] + for i, audio in enumerate(audio_segments): + if audio is not None: + final_audio_parts.append(audio.squeeze(0)) + # 添加暂停（如果有） + if i < len(pause_info) and pause_info[i] > 0: + silence = create_silence(pause_info[i], self.sr) + final_audio_parts.append(silence.squeeze(0)) + + if final_audio_parts: + final_audio = torch.cat(final_audio_parts, dim=0) + return final_audio.unsqueeze(0) + else: + return create_silence(0.1, self.sr) + + + + + + + + + + def _generate_segments_async(self, text_list, cfg_weight, temperature, repetition_penalty, min_p, top_p, disable_watermark, max_workers): + """异步并行生成文本片段 - 核心性能优化""" + audio_results = [None] * len(text_list) + result_queue = queue.Queue() + + def generate_worker(index, text): + try: + if text and text.strip(): + # 预处理文本 + text = punc_norm(text.strip()) + # 生成音频 + audio = self._generate_single_segment( + text, cfg_weight, temperature, repetition_penalty, min_p, top_p, disable_watermark + ) + else: + audio = create_silence(0.1, self.sr) + result_queue.put((index, audio, None)) + except Exception as e: + result_queue.put((index, None, str(e))) + + # 并发执行 + with ThreadPoolExecutor(max_workers=min(max_workers, len(text_list))) as executor: + futures = [executor.submit(generate_worker, i, text) for i, text in enumerate(text_list)] + + # 等待完成 + for future in as_completed(futures): + future.result() + + # 收集结果 + while not result_queue.empty(): + index, audio, error = result_queue.get() + if error: + audio_results[index] = create_silence(0.1, self.sr) + else: + audio_results[index] = audio + + return audio_results + + def _clean_audio_segments_batch(self, audio_segments, ae_threshold, ae_margin): + """批量清理音频片段的artifacts""" + cleaned_segments = [] + temp_files_to_cleanup = [] + + try: + for i, audio in enumerate(audio_segments): + if audio is not None: + # 保存临时音频文件 + import tempfile + with tempfile.NamedTemporaryFile(suffix=f'_segment_{i}.wav', delete=False) as temp_file: + temp_audio_path = temp_file.name + temp_files_to_cleanup.append(temp_audio_path) + torchaudio.save(temp_audio_path, audio, self.sr) + + # 清理artifacts + cleaned_audio_path = self._clean_artifacts(temp_audio_path, ae_threshold, ae_margin) + if cleaned_audio_path != temp_audio_path: + temp_files_to_cleanup.append(cleaned_audio_path) + + # 加载清理后的音频 + try: + if cleaned_audio_path != temp_audio_path: + cleaned_audio, _ = torchaudio.load(cleaned_audio_path) + cleaned_segments.append(cleaned_audio) + else: + # 清理失败，使用原音频 + cleaned_segments.append(audio) + except Exception as e: + print(f"[WARNING] Unable to load 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Update documentation strings to reflect English parameters and return values. --- src/chatterbox/text_utils.py | 88 ++++++++++++++++++------------------ src/chatterbox/tts.py | 52 ++++++++++----------- 2 files changed, 70 insertions(+), 70 deletions(-) diff --git a/src/chatterbox/text_utils.py b/src/chatterbox/text_utils.py index 4dba8234..615dc17c 100644 --- a/src/chatterbox/text_utils.py +++ b/src/chatterbox/text_utils.py @@ -1,7 +1,7 @@ """ -文本处理工具函数 +Text processing utility functions -包含长文本拆分、句子合并等文本处理功能 +Contains long text splitting, sentence merging and other text processing functions """ import re @@ -11,16 +11,16 @@ def split_by_word_boundary(text: str, max_len: int) -> List[str]: """ - 按单词边界拆分文本，确保不会在单词中间断开 + Split text by word boundaries to ensure words are not broken in the middle - 参数: - text: 要拆分的文本 - max_len: 每个分段的最大长度 + Parameters: + text: Text to split + max_len: Maximum length of each segment - 返回: - 文本分段列表 + Returns: + List of text segments """ - # 首先尝试在标点符号处分割 + # First try to split at punctuation marks punct_pattern = r'(?<=[.!?,;:])\s+' punct_splits = re.split(punct_pattern, text) @@ -30,12 +30,12 @@ def split_by_word_boundary(text: str, max_len: int) -> List[str]: segments.append(split) continue - # 如果标点分割后的片段仍然过长，按单词边界拆分 + # If the segment after punctuation splitting is still too long, split by word boundaries words = split.split() current = "" for word in words: - # 如果单词本身超过最大长度，尝试保留完整单词 + # If the word itself exceeds maximum length, try to keep the complete word if len(word) > max_len: if current: segments.append(current) @@ -43,7 +43,7 @@ def split_by_word_boundary(text: str, max_len: int) -> List[str]: current = "" continue - # 检查添加这个单词是否会超过最大长度 + # Check if adding this word would exceed maximum length potential_segment = (current + " " + word).strip() if current else word if len(potential_segment) > max_len: if current: @@ -52,7 +52,7 @@ def split_by_word_boundary(text: str, max_len: int) -> List[str]: else: current = potential_segment - # 添加最后剩余的部分 + # Add the remaining part if current: segments.append(current) @@ -61,15 +61,15 @@ def split_by_word_boundary(text: str, max_len: int) -> List[str]: def merge_short_sentences(sentences: List[str], max_length: int, min_length: int = 20) -> List[str]: """ - 合并短句子到下一句，确保不超过最大长度限制 + Merge short sentences to the next sentence, ensuring not to exceed maximum length limit - 参数: - sentences: 句子列表 - max_length: 合并后句子的最大长度限制 - min_length: 短句子的长度阈值 + Parameters: + sentences: List of sentences + max_length: Maximum length limit after merging + min_length: Length threshold for short sentences - 返回: - 合并后的句子列表 + Returns: + List of merged sentences """ if not sentences: return [] @@ -80,18 +80,18 @@ def merge_short_sentences(sentences: List[str], max_length: int, min_length: int while i < len(sentences): current = sentences[i].strip() - # 跳过空句子 + # Skip empty sentences if not current: i += 1 continue - # 如果当前句子长度大于等于min_length，直接添加到结果中 + # If current sentence length is greater than or equal to min_length, add directly to result if len(current) >= min_length: result.append(current) i += 1 continue - # 当前句子是短句，尝试与后面的句子合并 + # Current sentence is short, try to merge with subsequent sentences merged = current j = i + 1 while j < len(sentences) and len(merged) < min_length: @@ -100,20 +100,20 @@ def merge_short_sentences(sentences: List[str], max_length: int, min_length: int j += 1 continue - # 检查合并后是否超过最大长度 + # Check if merging would exceed maximum length potential_merge = merged + " " + next_sentence if len(potential_merge) <= max_length: merged = potential_merge j += 1 else: - # 如果合并会超过最大长度，停止合并 + # If merging would exceed maximum length, stop merging break - # 添加合并后的结果 + # Add merged result if merged: result.append(merged) - # 更新索引 + # Update index i = j if j > i else i + 1 return result @@ -121,29 +121,29 @@ def merge_short_sentences(sentences: List[str], max_length: int, min_length: int def split_text_into_segments(text: str, max_length: int = 300, logger: Optional[logging.Logger] = None) -> List[str]: """ - 将文本拆分成适合TTS处理的段落 + Split text into segments suitable for TTS processing - 参数: - text: 要拆分的文本 - max_length: 每个段落的最大字符数 - logger: 可选的日志记录器 + Parameters: + text: Text to split + max_length: Maximum character count for each segment + logger: Optional logger - 返回: - 拆分后的段落列表 + Returns: + List of split segments """ if not text or not text.strip(): return [] text = text.strip() - # 如果文本长度不超过最大长度，直接返回 + # If text length doesn't exceed maximum length, return directly if len(text) <= max_length: return [text] if logger: - logger.debug(f"文本长度 {len(text)} 超过最大限制 {max_length}，开始拆分") + logger.debug(f"Text length {len(text)} exceeds maximum limit {max_length}, starting split") - # 首先按段落分割（双换行符） + # First split by paragraphs (double newlines) paragraphs = [p.strip() for p in re.split(r'\n\s*\n', text) if p.strip()] segments = [] @@ -152,27 +152,27 @@ def split_text_into_segments(text: str, max_length: int = 300, logger: Optional[ segments.append(paragraph) continue - # 段落过长，进一步拆分 - # 1. 尝试按句子分割（句号、问号、感叹号） + # Paragraph too long, split further + # 1. Try to split by sentences (period, question mark, exclamation mark) sentences = re.split(r'(?<=[.!?])\s+', paragraph) sentences = [s.strip() for s in sentences if s.strip()] - # 2. 合并短句子，避免产生太多碎片 + # 2. Merge short sentences to avoid producing too many fragments sentences = merge_short_sentences(sentences, max_length, min_length=30) - # 3. 处理仍然过长的句子 + # 3. Handle sentences that are still too long for sentence in sentences: if len(sentence) <= max_length: segments.append(sentence) else: - # 使用单词边界拆分 + # Use word boundary splitting word_segments = split_by_word_boundary(sentence, max_length) segments.extend(word_segments) - # 清理空段落 + # Clean empty paragraphs segments = [s.strip() for s in segments if s.strip()] if logger: - logger.debug(f"文本拆分完成，共 {len(segments)} 个段落") + logger.debug(f"Text splitting completed, total {len(segments)} segments") return segments \ No newline at end of file diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index e974693b..b629fa2d 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -231,7 +231,7 @@ def generate( ae_margin=0.2, disable_watermark=False, max_segment_length=300, - max_workers=None, # None表示自动检测最优值 + max_workers=None, # None means auto-detect optimal value ): if audio_prompt_path: self.prepare_conditionals(audio_prompt_path, exaggeration=exaggeration) @@ -247,11 +247,11 @@ def generate( emotion_adv=exaggeration * torch.ones(1, 1, 1), ).to(device=self.device) - # 长文本处理：根据文本长度自动判断是否需要拆分处理 + # Long text processing: automatically determine if text needs to be split based on length if len(text) > max_segment_length: - # 使用异步批量生成 + # Use async batch generation if max_workers is None: - max_workers = 3 # 简单默认值 + max_workers = 3 # Simple default value return self._generate_long_text_async( text, max_segment_length=max_segment_length, @@ -387,22 +387,22 @@ def _generate_long_text_async( max_workers=3 ): """ - 异步生成长文本音频 - 核心优化只保留异步批量生成 + Async generation of long text audio - core optimization only keeps async batch generation """ - # 拆分文本为短段落 + # Split text into short paragraphs text_segments = split_text_into_segments(text, max_segment_length) if not text_segments: return create_silence(0.1, self.sr) - # 预处理文本片段，保留暂停标签信息 + # Preprocess text segments, preserve pause tag information all_segments = [] # [(text, pause_duration), ...] for segment_text in text_segments: - # 处理pause tags，保留暂停信息 + # Process pause tags, preserve pause information segments = parse_pause_tags(segment_text) all_segments.extend(segments) - # 分离文本和暂停信息 + # Separate text and pause information text_parts = [] pause_info = [] for text_part, pause_duration in all_segments: @@ -410,24 +410,24 @@ def _generate_long_text_async( text_parts.append(text_part.strip()) pause_info.append(pause_duration) - # 核心：异步批量生成 + # Core: async batch generation audio_segments = self._generate_segments_async( text_parts, cfg_weight, temperature, repetition_penalty, min_p, top_p, disable_watermark, max_workers ) - # 应用音频清理（如果启用） + # Apply audio cleaning (if enabled) if use_auto_editor: audio_segments = self._clean_audio_segments_batch( audio_segments, ae_threshold, ae_margin ) - # 合并音频，包括暂停 + # Merge audio, including pauses final_audio_parts = [] for i, audio in enumerate(audio_segments): if audio is not None: final_audio_parts.append(audio.squeeze(0)) - # 添加暂停（如果有） + # Add pause (if any) if i < len(pause_info) and pause_info[i] > 0: silence = create_silence(pause_info[i], self.sr) final_audio_parts.append(silence.squeeze(0)) @@ -447,16 +447,16 @@ def _generate_long_text_async( def _generate_segments_async(self, text_list, cfg_weight, temperature, repetition_penalty, min_p, top_p, disable_watermark, max_workers): - """异步并行生成文本片段 - 核心性能优化""" + """Async parallel generation of text segments - core performance optimization""" audio_results = [None] * len(text_list) result_queue = queue.Queue() def generate_worker(index, text): try: if text and text.strip(): - # 预处理文本 + # Preprocess text text = punc_norm(text.strip()) - # 生成音频 + # Generate audio audio = self._generate_single_segment( text, cfg_weight, temperature, repetition_penalty, min_p, top_p, disable_watermark ) @@ -466,15 +466,15 @@ def generate_worker(index, text): except Exception as e: result_queue.put((index, None, str(e))) - # 并发执行 + # Concurrent execution with ThreadPoolExecutor(max_workers=min(max_workers, len(text_list))) as executor: futures = [executor.submit(generate_worker, i, text) for i, text in enumerate(text_list)] - # 等待完成 + # Wait for completion for future in as_completed(futures): future.result() - # 收集结果 + # Collect results while not result_queue.empty(): index, audio, error = result_queue.get() if error: @@ -485,32 +485,32 @@ def generate_worker(index, text): return audio_results def _clean_audio_segments_batch(self, audio_segments, ae_threshold, ae_margin): - """批量清理音频片段的artifacts""" + """Batch clean artifacts from audio segments""" cleaned_segments = [] temp_files_to_cleanup = [] try: for i, audio in enumerate(audio_segments): if audio is not None: - # 保存临时音频文件 + # Save temporary audio file import tempfile with tempfile.NamedTemporaryFile(suffix=f'_segment_{i}.wav', delete=False) as temp_file: temp_audio_path = temp_file.name temp_files_to_cleanup.append(temp_audio_path) torchaudio.save(temp_audio_path, audio, self.sr) - # 清理artifacts + # Clean artifacts cleaned_audio_path = self._clean_artifacts(temp_audio_path, ae_threshold, ae_margin) if cleaned_audio_path != temp_audio_path: temp_files_to_cleanup.append(cleaned_audio_path) - # 加载清理后的音频 + # Load cleaned audio try: if cleaned_audio_path != temp_audio_path: cleaned_audio, _ = torchaudio.load(cleaned_audio_path) cleaned_segments.append(cleaned_audio) else: - # 清理失败，使用原音频 + # Cleaning failed, use original audio cleaned_segments.append(audio) except Exception as e: print(f"[WARNING] Unable to load cleaned audio segment {i}: {e}") @@ -521,13 +521,13 @@ def _clean_audio_segments_batch(self, audio_segments, ae_threshold, ae_margin): return cleaned_segments finally: - # 清理所有临时文件 + # Clean up all temporary files for temp_file in temp_files_to_cleanup: if os.path.exists(temp_file): try: os.unlink(temp_file) except: - pass # 忽略清理错误 + pass # Ignore cleanup errors def _generate_single_segment(self, text, cfg_weight, temperature, repetition_penalty=1.2, min_p=0.05, top_p=1.0, disable_watermark=False): """Generate audio for a single text segment""" From 1c1fabbebef173d8fa239c4871f73471fc9e3bbf Mon Sep 17 00:00:00 2001 From: Lukin Date: Thu, 19 Jun 2025 06:29:18 +0000 Subject: [PATCH 10/10] Enhance text processing utilities in text_utils.py with multi-language support. Introduce language detection, sentence separator patterns, and punctuation handling for English, Chinese, Japanese, and Korean. Update split_by_word_boundary and merge_short_sentences functions to accommodate language-specific features, improving text segmentation for TTS processing. --- src/chatterbox/text_utils.py | 251 ++++++++++++++++++++++++++++++----- 1 file changed, 216 insertions(+), 35 deletions(-) diff --git a/src/chatterbox/text_utils.py b/src/chatterbox/text_utils.py index 615dc17c..77db8aaa 100644 --- a/src/chatterbox/text_utils.py +++ b/src/chatterbox/text_utils.py @@ -2,26 +2,112 @@ Text processing utility functions Contains long text splitting, sentence merging and other text processing functions +Support for multiple languages including English, Chinese, Japanese, Korean, etc. """ import re -from typing import List, Optional +from typing import List, Optional, Tuple import logging -def split_by_word_boundary(text: str, max_len: int) -> List[str]: +def detect_language(text: str) -> str: + """ + Simple language detection based on character patterns + + Parameters: + text: Text to detect language for + + Returns: + Language code: 'zh' (Chinese), 'ja' (Japanese), 'ko' (Korean), 'en' (English/others) + """ + # Count different character types + chinese_chars = len(re.findall(r'[\u4e00-\u9fff]', text)) # CJK Unified Ideographs + japanese_chars = len(re.findall(r'[\u3040-\u309f\u30a0-\u30ff]', text)) # Hiragana + Katakana + korean_chars = len(re.findall(r'[\uac00-\ud7af]', text)) # Hangul + + total_chars = len(re.sub(r'\s+', '', text)) + + if total_chars == 0: + return 'en' + + # Calculate ratios + chinese_ratio = chinese_chars / total_chars + japanese_ratio = japanese_chars / total_chars + korean_ratio = korean_chars / total_chars + + # Determine primary language + if chinese_ratio > 0.3: + return 'zh' + elif japanese_ratio > 0.3: + return 'ja' + elif korean_ratio > 0.3: + return 'ko' + else: + return 'en' + + +def get_sentence_separators(lang: str) -> str: + """ + Get sentence separator pattern for different languages + + Parameters: + lang: Language code + + Returns: + Regex pattern for sentence separators + """ + if lang == 'zh': + # Chinese punctuation + return r'(?<=[。！？；])\s*' + elif lang == 'ja': + # Japanese punctuation + return r'(?<=[。！？])\s*' + elif lang == 'ko': + # Korean punctuation + return r'(?<=[\.!?。！？])\s*' + else: + # English and other languages + return r'(?<=[.!?])\s+' + + +def get_punctuation_pattern(lang: str) -> str: + """ + Get punctuation pattern for word boundary splitting + + Parameters: + lang: Language code + + Returns: + Regex pattern for punctuation marks + """ + if lang == 'zh': + return r'(?<=[。！？，；：、])\s*' + elif lang == 'ja': + return r'(?<=[。！？、，])\s*' + elif lang == 'ko': + return r'(?<=[\.!?,;。！？，；])\s*' + else: + return r'(?<=[.!?,;:])\s+' + + +def split_by_word_boundary(text: str, max_len: int, lang: Optional[str] = None) -> List[str]: """ Split text by word boundaries to ensure words are not broken in the middle + Support for multiple languages Parameters: text: Text to split max_len: Maximum length of each segment + lang: Language code (auto-detect if None) Returns: List of text segments """ + if lang is None: + lang = detect_language(text) + # First try to split at punctuation marks - punct_pattern = r'(?<=[.!?,;:])\s+' + punct_pattern = get_punctuation_pattern(lang) punct_splits = re.split(punct_pattern, text) segments = [] @@ -30,43 +116,116 @@ def split_by_word_boundary(text: str, max_len: int) -> List[str]: segments.append(split) continue - # If the segment after punctuation splitting is still too long, split by word boundaries - words = split.split() - current = "" - - for word in words: - # If the word itself exceeds maximum length, try to keep the complete word - if len(word) > max_len: - if current: - segments.append(current) - segments.append(word) - current = "" - continue - - # Check if adding this word would exceed maximum length - potential_segment = (current + " " + word).strip() if current else word - if len(potential_segment) > max_len: - if current: - segments.append(current) - current = word - else: - current = potential_segment + # If the segment after punctuation splitting is still too long + if lang in ['zh', 'ja', 'ko']: + # For CJK languages, split by character for fine-grained control + segments.extend(_split_cjk_text(split, max_len)) + else: + # For space-separated languages, split by word boundaries + segments.extend(_split_spaced_text(split, max_len)) + + return segments + + +def _split_cjk_text(text: str, max_len: int) -> List[str]: + """ + Split CJK text by characters while trying to preserve semantic units + + Parameters: + text: CJK text to split + max_len: Maximum length of each segment + + Returns: + List of text segments + """ + segments = [] + current = "" + + # Try to break at common phrase boundaries + phrase_boundaries = r'[，、；：]' # Common CJK phrase separators + + i = 0 + while i < len(text): + char = text[i] - # Add the remaining part - if current: + # Check if adding this character would exceed max length + if len(current + char) > max_len: + if current: + segments.append(current) + current = char + else: + # Single character exceeds max length, add it anyway + segments.append(char) + current = "" + else: + current += char + + # Try to break at phrase boundaries if we're getting close to max length + if (len(current) > max_len * 0.8 and + re.search(phrase_boundaries, char) and + i < len(text) - 1): segments.append(current) + current = "" + + i += 1 + + # Add the remaining part + if current: + segments.append(current) + + return segments + + +def _split_spaced_text(text: str, max_len: int) -> List[str]: + """ + Split space-separated text by word boundaries + + Parameters: + text: Text to split + max_len: Maximum length of each segment + + Returns: + List of text segments + """ + segments = [] + words = text.split() + current = "" + + for word in words: + # If the word itself exceeds maximum length, try to keep the complete word + if len(word) > max_len: + if current: + segments.append(current) + segments.append(word) + current = "" + continue + # Check if adding this word would exceed maximum length + potential_segment = (current + " " + word).strip() if current else word + if len(potential_segment) > max_len: + if current: + segments.append(current) + current = word + else: + current = potential_segment + + # Add the remaining part + if current: + segments.append(current) + return segments -def merge_short_sentences(sentences: List[str], max_length: int, min_length: int = 20) -> List[str]: +def merge_short_sentences(sentences: List[str], max_length: int, min_length: int = 20, lang: Optional[str] = None) -> List[str]: """ Merge short sentences to the next sentence, ensuring not to exceed maximum length limit + Support for multiple languages Parameters: sentences: List of sentences max_length: Maximum length limit after merging min_length: Length threshold for short sentences + lang: Language code (auto-detect if None) Returns: List of merged sentences @@ -74,6 +233,20 @@ def merge_short_sentences(sentences: List[str], max_length: int, min_length: int if not sentences: return [] + # Auto-detect language from first non-empty sentence + if lang is None: + for sentence in sentences: + if sentence.strip(): + lang = detect_language(sentence) + break + else: + lang = 'en' + + # Adjust min_length for different languages + # CJK languages are more information-dense + if lang in ['zh', 'ja', 'ko']: + min_length = max(10, min_length // 2) + result = [] i = 0 @@ -101,7 +274,8 @@ def merge_short_sentences(sentences: List[str], max_length: int, min_length: int continue # Check if merging would exceed maximum length - potential_merge = merged + " " + next_sentence + separator = "" if lang in ['zh', 'ja', 'ko'] else " " + potential_merge = merged + separator + next_sentence if len(potential_merge) <= max_length: merged = potential_merge j += 1 @@ -122,6 +296,7 @@ def merge_short_sentences(sentences: List[str], max_length: int, min_length: int def split_text_into_segments(text: str, max_length: int = 300, logger: Optional[logging.Logger] = None) -> List[str]: """ Split text into segments suitable for TTS processing + Support for multiple languages including English, Chinese, Japanese, Korean Parameters: text: Text to split @@ -136,12 +311,15 @@ def split_text_into_segments(text: str, max_length: int = 300, logger: Optional[ text = text.strip() + # Auto-detect language + lang = detect_language(text) + # If text length doesn't exceed maximum length, return directly if len(text) <= max_length: return [text] if logger: - logger.debug(f"Text length {len(text)} exceeds maximum limit {max_length}, starting split") + logger.debug(f"Text length {len(text)} exceeds maximum limit {max_length}, starting split (language: {lang})") # First split by paragraphs (double newlines) paragraphs = [p.strip() for p in re.split(r'\n\s*\n', text) if p.strip()] @@ -153,26 +331,29 @@ def split_text_into_segments(text: str, max_length: int = 300, logger: Optional[ continue # Paragraph too long, split further - # 1. Try to split by sentences (period, question mark, exclamation mark) - sentences = re.split(r'(?<=[.!?])\s+', paragraph) + # 1. Try to split by sentences using language-appropriate patterns + sentence_pattern = get_sentence_separators(lang) + sentences = re.split(sentence_pattern, paragraph) sentences = [s.strip() for s in sentences if s.strip()] # 2. Merge short sentences to avoid producing too many fragments - sentences = merge_short_sentences(sentences, max_length, min_length=30) + # Adjust min_length based on language characteristics + min_length = 30 if lang == 'en' else 15 # CJK languages are more dense + sentences = merge_short_sentences(sentences, max_length, min_length=min_length, lang=lang) # 3. Handle sentences that are still too long for sentence in sentences: if len(sentence) <= max_length: segments.append(sentence) else: - # Use word boundary splitting - word_segments = split_by_word_boundary(sentence, max_length) + # Use word boundary splitting with language support + word_segments = split_by_word_boundary(sentence, max_length, lang=lang) segments.extend(word_segments) # Clean empty paragraphs segments = [s.strip() for s in segments if s.strip()] if logger: - logger.debug(f"Text splitting completed, total {len(segments)} segments") + logger.debug(f"Text splitting completed, total {len(segments)} segments (language: {lang})") return segments \ No newline at end of file