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inference-cli.py

@@ -93,17 +93,6 @@ wave_path = Path(output_dir)/"out.wav"
 spectrogram_path = Path(output_dir)/"out.png"
 vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"
 
-SPLIT_WORDS = [
-    "but", "however", "nevertheless", "yet", "still",
-    "therefore", "thus", "hence", "consequently",
-    "moreover", "furthermore", "additionally",
-    "meanwhile", "alternatively", "otherwise",
-    "namely", "specifically", "for example", "such as",
-    "in fact", "indeed", "notably",
-    "in contrast", "on the other hand", "conversely",
-    "in conclusion", "to summarize", "finally"
-]
-
 device = (
     "cuda"
     if torch.cuda.is_available()
@@ -167,103 +156,36 @@ F5TTS_model_cfg = dict(
 )
 E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
 
-def split_text_into_batches(text, max_chars=200, split_words=SPLIT_WORDS):
-    if len(text.encode('utf-8')) <= max_chars:
-        return [text]
-    if text[-1] not in ['。', '.', '!', '！', '?', '？']:
-        text += '.'
-        
-    sentences = re.split('([。.!?！？])', text)
-    sentences = [''.join(i) for i in zip(sentences[0::2], sentences[1::2])]
-    
-    batches = []
-    current_batch = ""
-    
-    def split_by_words(text):
-        words = text.split()
-        current_word_part = ""
-        word_batches = []
-        for word in words:
-            if len(current_word_part.encode('utf-8')) + len(word.encode('utf-8')) + 1 <= max_chars:
-                current_word_part += word + ' '
-            else:
-                if current_word_part:
-                    # Try to find a suitable split word
-                    for split_word in split_words:
-                        split_index = current_word_part.rfind(' ' + split_word + ' ')
-                        if split_index != -1:
-                            word_batches.append(current_word_part[:split_index].strip())
-                            current_word_part = current_word_part[split_index:].strip() + ' '
-                            break
-                    else:
-                        # If no suitable split word found, just append the current part
-                        word_batches.append(current_word_part.strip())
-                        current_word_part = ""
-                current_word_part += word + ' '
-        if current_word_part:
-            word_batches.append(current_word_part.strip())
-        return word_batches
+
+def chunk_text(text, max_chars=135):
+    """
+    Splits the input text into chunks, each with a maximum number of characters.
+    Args:
+        text (str): The text to be split.
+        max_chars (int): The maximum number of characters per chunk.
+    Returns:
+        List[str]: A list of text chunks.
+    """
+    chunks = []
+    current_chunk = ""
+    # Split the text into sentences based on punctuation followed by whitespace
+    sentences = re.split(r'(?<=[;:,.!?])\s+|(?<=[；：，。！？])', text)
 
     for sentence in sentences:
-        if len(current_batch.encode('utf-8')) + len(sentence.encode('utf-8')) <= max_chars:
-            current_batch += sentence
+        if len(current_chunk.encode('utf-8')) + len(sentence.encode('utf-8')) <= max_chars:
+            current_chunk += sentence + " " if sentence and len(sentence[-1].encode('utf-8')) == 1 else sentence
         else:
-            # If adding this sentence would exceed the limit
-            if current_batch:
-                batches.append(current_batch)
-                current_batch = ""
-            
-            # If the sentence itself is longer than max_chars, split it
-            if len(sentence.encode('utf-8')) > max_chars:
-                # First, try to split by colon
-                colon_parts = sentence.split(':')
-                if len(colon_parts) > 1:
-                    for part in colon_parts:
-                        if len(part.encode('utf-8')) <= max_chars:
-                            batches.append(part)
-                        else:
-                            # If colon part is still too long, split by comma
-                            comma_parts = re.split('[,，]', part)
-                            if len(comma_parts) > 1:
-                                current_comma_part = ""
-                                for comma_part in comma_parts:
-                                    if len(current_comma_part.encode('utf-8')) + len(comma_part.encode('utf-8')) <= max_chars:
-                                        current_comma_part += comma_part + ','
-                                    else:
-                                        if current_comma_part:
-                                            batches.append(current_comma_part.rstrip(','))
-                                        current_comma_part = comma_part + ','
-                                if current_comma_part:
-                                    batches.append(current_comma_part.rstrip(','))
-                            else:
-                                # If no comma, split by words
-                                batches.extend(split_by_words(part))
-                else:
-                    # If no colon, split by comma
-                    comma_parts = re.split('[,，]', sentence)
-                    if len(comma_parts) > 1:
-                        current_comma_part = ""
-                        for comma_part in comma_parts:
-                            if len(current_comma_part.encode('utf-8')) + len(comma_part.encode('utf-8')) <= max_chars:
-                                current_comma_part += comma_part + ','
-                            else:
-                                if current_comma_part:
-                                    batches.append(current_comma_part.rstrip(','))
-                                current_comma_part = comma_part + ','
-                        if current_comma_part:
-                            batches.append(current_comma_part.rstrip(','))
-                    else:
-                        # If no comma, split by words
-                        batches.extend(split_by_words(sentence))
-            else:
-                current_batch = sentence
-    
-    if current_batch:
-        batches.append(current_batch)
-    
-    return batches
+            if current_chunk:
+                chunks.append(current_chunk.strip())
+            current_chunk = sentence + " " if sentence and len(sentence[-1].encode('utf-8')) == 1 else sentence
+
+    if current_chunk:
+        chunks.append(current_chunk.strip())
+
+    return chunks
+
 
-def infer_batch(ref_audio, ref_text, gen_text_batches, model, remove_silence):
+def infer_batch(ref_audio, ref_text, gen_text_batches, model, remove_silence, cross_fade_duration=0.15):
     if model == "F5-TTS":
         ema_model = load_model(model, "F5TTS_Base", DiT, F5TTS_model_cfg, 1200000)
     elif model == "E2-TTS":
@@ -321,8 +243,44 @@ def infer_batch(ref_audio, ref_text, gen_text_batches, model, remove_silence):
         generated_waves.append(generated_wave)
         spectrograms.append(generated_mel_spec[0].cpu().numpy())
 
-    # Combine all generated waves
-    final_wave = np.concatenate(generated_waves)
+    # Combine all generated waves with cross-fading
+    if cross_fade_duration <= 0:
+        # Simply concatenate
+        final_wave = np.concatenate(generated_waves)
+    else:
+        final_wave = generated_waves[0]
+        for i in range(1, len(generated_waves)):
+            prev_wave = final_wave
+            next_wave = generated_waves[i]
+
+            # Calculate cross-fade samples, ensuring it does not exceed wave lengths
+            cross_fade_samples = int(cross_fade_duration * target_sample_rate)
+            cross_fade_samples = min(cross_fade_samples, len(prev_wave), len(next_wave))
+
+            if cross_fade_samples <= 0:
+                # No overlap possible, concatenate
+                final_wave = np.concatenate([prev_wave, next_wave])
+                continue
+
+            # Overlapping parts
+            prev_overlap = prev_wave[-cross_fade_samples:]
+            next_overlap = next_wave[:cross_fade_samples]
+
+            # Fade out and fade in
+            fade_out = np.linspace(1, 0, cross_fade_samples)
+            fade_in = np.linspace(0, 1, cross_fade_samples)
+
+            # Cross-faded overlap
+            cross_faded_overlap = prev_overlap * fade_out + next_overlap * fade_in
+
+            # Combine
+            new_wave = np.concatenate([
+                prev_wave[:-cross_fade_samples],
+                cross_faded_overlap,
+                next_wave[cross_fade_samples:]
+            ])
+
+            final_wave = new_wave
 
     with open(wave_path, "wb") as f:
         sf.write(f.name, final_wave, target_sample_rate)
@@ -343,11 +301,7 @@ def infer_batch(ref_audio, ref_text, gen_text_batches, model, remove_silence):
     print(spectrogram_path)
 
 
-def infer(ref_audio_orig, ref_text, gen_text, model, remove_silence, custom_split_words):
-    if not custom_split_words.strip():
-        custom_words = [word.strip() for word in custom_split_words.split(',')]
-        global SPLIT_WORDS
-        SPLIT_WORDS = custom_words
+def infer(ref_audio_orig, ref_text, gen_text, model, remove_silence, cross_fade_duration=0.15):
 
     print(gen_text)
 
@@ -355,7 +309,7 @@ def infer(ref_audio_orig, ref_text, gen_text, model, remove_silence, custom_spli
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
         aseg = AudioSegment.from_file(ref_audio_orig)
 
-        non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500)
+        non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=1000)
         non_silent_wave = AudioSegment.silent(duration=0)
         for non_silent_seg in non_silent_segs:
             non_silent_wave += non_silent_seg
@@ -387,16 +341,23 @@ def infer(ref_audio_orig, ref_text, gen_text, model, remove_silence, custom_spli
     else:
         print("Using custom reference text...")
 
+    # Add the functionality to ensure it ends with ". "
+    if not ref_text.endswith(". ") and not ref_text.endswith("。"):
+        if ref_text.endswith("."):
+            ref_text += " "
+        else:
+            ref_text += ". "
+
     # Split the input text into batches
     audio, sr = torchaudio.load(ref_audio)
-    max_chars = int(len(ref_text.encode('utf-8')) / (audio.shape[-1] / sr) * (30 - audio.shape[-1] / sr))
-    gen_text_batches = split_text_into_batches(gen_text, max_chars=max_chars)
+    max_chars = int(len(ref_text.encode('utf-8')) / (audio.shape[-1] / sr) * (25 - audio.shape[-1] / sr))
+    gen_text_batches = chunk_text(gen_text, max_chars=max_chars)
     print('ref_text', ref_text)
     for i, gen_text in enumerate(gen_text_batches):
         print(f'gen_text {i}', gen_text)
     
     print(f"Generating audio using {model} in {len(gen_text_batches)} batches, loading models...")
-    return infer_batch((audio, sr), ref_text, gen_text_batches, model, remove_silence)
+    return infer_batch((audio, sr), ref_text, gen_text_batches, model, remove_silence, cross_fade_duration)
     
 
-infer(ref_audio, ref_text, gen_text, model, remove_silence, ",".join(SPLIT_WORDS))
+infer(ref_audio, ref_text, gen_text, model, remove_silence)

inference-cli.toml

@@ -6,5 +6,5 @@ ref_text = "Some call me nature, others call me mother nature."
 gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences."
 # File with text to generate. Ignores the text above.
 gen_file = ""
-remove_silence = true
+remove_silence = false
 output_dir = "tests"