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app.py

@@ -112,101 +112,34 @@ E2TTS_ema_model = load_model(
     "E2-TTS", "E2TTS_Base", UNetT, E2TTS_model_cfg, 1200000
 )
 
-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) + len(sentence) <= max_chars:
+            current_chunk += 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 current_chunk:
+        chunks.append(current_chunk.strip())
+
+    return chunks
 
 @gpu_decorator
 def infer_batch(ref_audio, ref_text, gen_text_batches, exp_name, remove_silence, progress=gr.Progress()):
@@ -306,7 +239,9 @@ def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, custom_s
     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=500
+        )
         non_silent_wave = AudioSegment.silent(duration=0)
         for non_silent_seg in non_silent_segs:
             non_silent_wave += non_silent_seg
@@ -332,13 +267,20 @@ def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, custom_s
     else:
         gr.Info("Using custom reference text...")
 
-    # Split the input text into batches
+    # Add the functionality to ensure it ends with ". "
+    if not ref_text.endswith(". "):
+        if ref_text.endswith("."):
+            ref_text += " "
+        else:
+            ref_text += ". "
+
     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)
+
+    # Use the new chunk_text function to split gen_text
+    gen_text_batches = chunk_text(gen_text, max_chars=135)
     print('ref_text', ref_text)
-    for i, gen_text in enumerate(gen_text_batches):
-        print(f'gen_text {i}', gen_text)
+    for i, batch_text in enumerate(gen_text_batches):
+        print(f'gen_text {i}', batch_text)
     
     gr.Info(f"Generating audio using {exp_name} in {len(gen_text_batches)} batches")
     return infer_batch((audio, sr), ref_text, gen_text_batches, exp_name, remove_silence)
@@ -823,4 +765,4 @@ def main(port, host, share, api):
 
 
 if __name__ == "__main__":
-    main()
+    main()