From 555e146fb4066d465225a850b21243e5b7eccd0e Mon Sep 17 00:00:00 2001 From: Arcitec <38923130+Arcitec@users.noreply.github.com> Date: Thu, 11 Sep 2025 00:06:05 +0200 Subject: [PATCH] feat(webui): Implement speech synthesis progress bar --- indextts/infer.py | 22 +++++++++++----------- indextts/infer_v2.py | 16 ++++++++++------ 2 files changed, 21 insertions(+), 17 deletions(-) diff --git a/indextts/infer.py b/indextts/infer.py index c776b74..f137f25 100644 --- a/indextts/infer.py +++ b/indextts/infer.py @@ -291,9 +291,9 @@ class IndexTTS: - 越大，bucket数量越少，batch越多，推理速度越*快*，占用内存更多，可能影响质量 - 越小，bucket数量越多，batch越少，推理速度越*慢*，占用内存和质量更接近于非快速推理 """ - print(">> start fast inference...") + print(">> starting fast inference...") - self._set_gr_progress(0, "start fast inference...") + self._set_gr_progress(0, "starting fast inference...") if verbose: print(f"origin text:{text}") start_time = time.perf_counter() @@ -385,7 +385,7 @@ class IndexTTS: processed_num += batch_num # gpt speech self._set_gr_progress(0.2 + 0.3 * processed_num / all_batch_num, - f"gpt inference speech... {processed_num}/{all_batch_num}") + f"gpt speech inference {processed_num}/{all_batch_num}...") m_start_time = time.perf_counter() with torch.no_grad(): with torch.amp.autocast(batch_text_tokens.device.type, enabled=self.dtype is not None, @@ -407,7 +407,7 @@ class IndexTTS: gpt_gen_time += time.perf_counter() - m_start_time # gpt latent - self._set_gr_progress(0.5, "gpt inference latents...") + self._set_gr_progress(0.5, "gpt latents inference...") all_idxs = [] all_latents = [] has_warned = False @@ -456,7 +456,7 @@ class IndexTTS: latent_length = len(all_latents) # bigvgan chunk decode - self._set_gr_progress(0.7, "bigvgan decode...") + self._set_gr_progress(0.7, "bigvgan decoding...") tqdm_progress = tqdm(total=latent_length, desc="bigvgan") for items in chunk_latents: tqdm_progress.update(len(items)) @@ -478,7 +478,7 @@ class IndexTTS: self.torch_empty_cache() # wav audio output - self._set_gr_progress(0.9, "save audio...") + self._set_gr_progress(0.9, "saving audio...") wav = torch.cat(wavs, dim=1) wav_length = wav.shape[-1] / sampling_rate print(f">> Reference audio length: {cond_mel_frame * 256 / sampling_rate:.2f} seconds") @@ -509,8 +509,8 @@ class IndexTTS: # 原始推理模式 def infer(self, audio_prompt, text, output_path, verbose=False, max_text_tokens_per_segment=120, **generation_kwargs): - print(">> start inference...") - self._set_gr_progress(0, "start inference...") + print(">> starting inference...") + self._set_gr_progress(0, "starting inference...") if verbose: print(f"origin text:{text}") start_time = time.perf_counter() @@ -578,7 +578,7 @@ class IndexTTS: # print(text_len) progress += 1 self._set_gr_progress(0.2 + 0.4 * (progress - 1) / len(segments), - f"gpt inference latent... {progress}/{len(segments)}") + f"gpt latents inference {progress}/{len(segments)}...") m_start_time = time.perf_counter() with torch.no_grad(): with torch.amp.autocast(text_tokens.device.type, enabled=self.dtype is not None, dtype=self.dtype): @@ -620,7 +620,7 @@ class IndexTTS: print(f"fix codes shape: {codes.shape}, codes type: {codes.dtype}") print(f"code len: {code_lens}") self._set_gr_progress(0.2 + 0.4 * progress / len(segments), - f"gpt inference speech... {progress}/{len(segments)}") + f"gpt speech inference {progress}/{len(segments)}...") m_start_time = time.perf_counter() # latent, text_lens_out, code_lens_out = \ with torch.amp.autocast(text_tokens.device.type, enabled=self.dtype is not None, dtype=self.dtype): @@ -644,7 +644,7 @@ class IndexTTS: # wavs.append(wav[:, :-512]) wavs.append(wav.cpu()) # to cpu before saving end_time = time.perf_counter() - self._set_gr_progress(0.9, "save audio...") + self._set_gr_progress(0.9, "saving audio...") wav = torch.cat(wavs, dim=1) wav_length = wav.shape[-1] / sampling_rate print(f">> Reference audio length: {cond_mel_frame * 256 / sampling_rate:.2f} seconds") diff --git a/indextts/infer_v2.py b/indextts/infer_v2.py index 7161a58..880fe33 100644 --- a/indextts/infer_v2.py +++ b/indextts/infer_v2.py @@ -298,8 +298,8 @@ class IndexTTS2: emo_vector=None, use_emo_text=False, emo_text=None, use_random=False, interval_silence=200, verbose=False, max_text_tokens_per_segment=120, **generation_kwargs): - print(">> start inference...") - self._set_gr_progress(0, "start inference...") + print(">> starting inference...") + self._set_gr_progress(0, "starting inference...") if verbose: print(f"origin text:{text}, spk_audio_prompt:{spk_audio_prompt}," f" emo_audio_prompt:{emo_audio_prompt}, emo_alpha:{emo_alpha}, " @@ -400,9 +400,10 @@ class IndexTTS2: self._set_gr_progress(0.1, "text processing...") text_tokens_list = self.tokenizer.tokenize(text) segments = self.tokenizer.split_segments(text_tokens_list, max_text_tokens_per_segment) + segments_count = len(segments) if verbose: print("text_tokens_list:", text_tokens_list) - print("segments count:", len(segments)) + print("segments count:", segments_count) print("max_text_tokens_per_segment:", max_text_tokens_per_segment) print(*segments, sep="\n") do_sample = generation_kwargs.pop("do_sample", True) @@ -421,9 +422,11 @@ class IndexTTS2: gpt_forward_time = 0 s2mel_time = 0 bigvgan_time = 0 - progress = 0 has_warned = False - for sent in segments: + for seg_idx, sent in enumerate(segments): + self._set_gr_progress(0.2 + 0.7 * seg_idx / segments_count, + f"speech synthesis {seg_idx + 1}/{segments_count}...") + text_tokens = self.tokenizer.convert_tokens_to_ids(sent) text_tokens = torch.tensor(text_tokens, dtype=torch.int32, device=self.device).unsqueeze(0) if verbose: @@ -553,7 +556,8 @@ class IndexTTS2: # wavs.append(wav[:, :-512]) wavs.append(wav.cpu()) # to cpu before saving end_time = time.perf_counter() - self._set_gr_progress(0.9, "save audio...") + + self._set_gr_progress(0.9, "saving audio...") wavs = self.insert_interval_silence(wavs, sampling_rate=sampling_rate, interval_silence=interval_silence) wav = torch.cat(wavs, dim=1) wav_length = wav.shape[-1] / sampling_rate