updated pipeline

preprocess_v2.py

@@ -0,0 +1,127 @@
+import os
+import argparse
+import json
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--add_auxiliary_data", type=bool, help="Whether to add extra data as fine-tuning helper")
+    args = parser.parse_args()
+
+    new_annos = []
+    # Source 1: transcribed short audios
+    if os.path.exists("short_character_anno.txt"):
+        with open("short_character_anno.txt", 'r', encoding='utf-8') as f:
+            short_character_anno = f.readlines()
+            new_annos += short_character_anno
+    # Source 2: transcribed long audio segments
+    if os.path.exists("long_character_anno.txt"):
+        with open("long_character_anno.txt", 'r', encoding='utf-8') as f:
+            long_character_anno = f.readlines()
+            new_annos += long_character_anno
+
+    # Get all speaker names
+    speakers = []
+    for line in new_annos:
+        path, speaker, text = line.split("|")
+        if speaker not in speakers:
+            speakers.append(speaker)
+    assert (len(speakers) != 0), "no speaker found"
+    # Source 3 (Optional): sampled audios as extra training helpers
+    if args.add_auxiliary_data:
+        with open("sampled_audio4ft.txt", 'r', encoding='utf-8') as f:
+            old_annos = f.readlines()
+        num_old_voices = len(old_annos)
+        num_new_voices = len(new_annos)
+        # STEP 1: balance number of new & old voices
+        cc_duplicate = num_old_voices // num_new_voices
+        if cc_duplicate == 0:
+            cc_duplicate = 1
+
+
+        # STEP 2: modify config file
+        with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
+            hps = json.load(f)
+
+        # assign ids to new speakers
+        speaker2id = {}
+        for i, speaker in enumerate(speakers):
+            speaker2id[speaker] = hps['data']["n_speakers"] + i
+        # modify n_speakers
+        hps['data']["n_speakers"] = hps['data']["n_speakers"] + len(speakers)
+        # add speaker names
+        for speaker in speakers:
+            hps['speakers'][speaker] = speaker2id[speaker]
+        hps['train']['log_interval'] = 100
+        hps['train']['eval_interval'] = 1000
+        hps['train']['batch_size'] = 16
+        hps['data']['training_files'] = "final_annotation_train.txt"
+        hps['data']['validation_files'] = "final_annotation_val.txt"
+        # save modified config
+        with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
+            json.dump(hps, f, indent=2)
+
+        # STEP 3: clean annotations, replace speaker names with assigned speaker IDs
+        import text
+        cleaned_new_annos = []
+        for i, line in enumerate(new_annos):
+            path, speaker, txt = line.split("|")
+            if len(txt) > 150:
+                continue
+            cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
+            cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
+            cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
+        # merge with old annotation
+        final_annos = old_annos + cc_duplicate * cleaned_new_annos
+        # save annotation file
+        with open("final_annotation_train.txt", 'w', encoding='utf-8') as f:
+            for line in final_annos:
+                f.write(line)
+        # save annotation file for validation
+        with open("final_annotation_val.txt", 'w', encoding='utf-8') as f:
+            for line in cleaned_new_annos:
+                f.write(line)
+        print("finished")
+    else:
+        # Do not add extra helper data
+        # STEP 1: modify config file
+        with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
+            hps = json.load(f)
+
+        # assign ids to new speakers
+        speaker2id = {}
+        for i, speaker in enumerate(speakers):
+            speaker2id[speaker] = i
+        # modify n_speakers
+        hps['data']["n_speakers"] = len(speakers)
+        # overwrite speaker names
+        hps['speakers'] = speaker2id
+        hps['train']['log_interval'] = 10
+        hps['train']['eval_interval'] = 100
+        hps['train']['batch_size'] = 16
+        hps['data']['training_files'] = "final_annotation_train.txt"
+        hps['data']['validation_files'] = "final_annotation_val.txt"
+        # save modified config
+        with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
+            json.dump(hps, f, indent=2)
+
+        # STEP 2: clean annotations, replace speaker names with assigned speaker IDs
+        import text
+
+        cleaned_new_annos = []
+        for i, line in enumerate(new_annos):
+            path, speaker, txt = line.split("|")
+            if len(txt) > 150:
+                continue
+            cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
+            cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
+            cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
+
+        final_annos = cleaned_new_annos
+        # save annotation file
+        with open("final_annotation_train.txt", 'w', encoding='utf-8') as f:
+            for line in final_annos:
+                f.write(line)
+        # save annotation file for validation
+        with open("final_annotation_val.txt", 'w', encoding='utf-8') as f:
+            for line in cleaned_new_annos:
+                f.write(line)
+        print("finished")