File size: 4,064 Bytes
e412140 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | #!/usr/bin/env python3
"""
Step 2: metadata.tsv → FLAC-to-WAV conversion + metadata_kimi_train.jsonl
WARNING: This script converts all .flac files to .wav IN-PLACE and deletes the .flac.
WAV files are approximately 2-3x larger than FLAC.
Purpose: produce training data in Kimi-Audio conversation format.
Output: metadata_kimi_train.jsonl (same directory as this script)
Each pair produces two entries:
1. EN→ZH: user gives EN audio, assistant responds with ZH audio + ZH text
2. ZH→EN: user gives ZH audio, assistant responds with EN audio + EN text
"""
import csv
import json
import os
import soundfile as sf
from tqdm import tqdm
BASE_DIR = os.path.dirname(__file__)
INPUT_TSV = os.path.join(BASE_DIR, "metadata.tsv")
OUTPUT_JSONL = os.path.join(BASE_DIR, "metadata_kimi_train.jsonl")
def flac_to_wav(flac_path: str) -> str:
"""Convert a .flac file to .wav in-place (deletes .flac). Returns .wav path."""
wav_path = flac_path[:-5] + ".wav"
data, sr = sf.read(flac_path)
sf.write(wav_path, data, sr)
os.remove(flac_path)
return wav_path
def flac_rel_to_wav_rel(p: str) -> str:
return p[:-5] + ".wav" if p.endswith(".flac") else p
def main():
with open(INPUT_TSV, "r", encoding="utf-8") as f:
rows = list(csv.DictReader(f, delimiter="\t"))
print(f"Loaded {len(rows)} pairs from {INPUT_TSV}")
print("Converting FLAC → WAV (in-place) and writing metadata_kimi_train.jsonl ...")
ok = skip = fail = 0
out_f = open(OUTPUT_JSONL, "w", encoding="utf-8")
for row in tqdm(rows, unit="pairs"):
zh_flac = os.path.join(BASE_DIR, row["zh_path"])
en_flac = os.path.join(BASE_DIR, row["en_path"])
zh_text = row["zh_text"]
en_text = row["en_text"]
# Convert FLAC → WAV for both files
converted = {}
for key, flac_abs in [("zh", zh_flac), ("en", en_flac)]:
wav_abs = flac_abs[:-5] + ".wav"
if os.path.exists(wav_abs):
converted[key] = wav_abs
elif os.path.exists(flac_abs):
try:
converted[key] = flac_to_wav(flac_abs)
except Exception as e:
print(f"\n FAILED converting {flac_abs}: {e}")
fail += 1
break
else:
print(f"\n MISSING: {flac_abs}")
fail += 1
break
else:
# Both converted successfully — write two conversation entries
en_rel = flac_rel_to_wav_rel(row["en_path"])
zh_rel = flac_rel_to_wav_rel(row["zh_path"])
# EN → ZH
out_f.write(json.dumps({
"task_type": "s-s",
"conversation": [
{"role": "user", "message_type": "text", "content": "Translate the given English speech into Chinese while preserving its expressiveness."},
{"role": "user", "message_type": "audio", "content": en_rel},
{"role": "assistant", "message_type": "audio-text", "content": [zh_rel, zh_text]},
]
}, ensure_ascii=False) + "\n")
# ZH → EN
out_f.write(json.dumps({
"task_type": "s-s",
"conversation": [
{"role": "user", "message_type": "text", "content": "Translate the given Chinese speech into English while preserving its expressiveness."},
{"role": "user", "message_type": "audio", "content": zh_rel},
{"role": "assistant", "message_type": "audio-text", "content": [en_rel, en_text]},
]
}, ensure_ascii=False) + "\n")
ok += 1
continue
skip += 1
out_f.close()
print(f"\nDone: {ok} pairs converted, {skip} skipped/missing, {fail} errors")
print(f"Output: {OUTPUT_JSONL} ({ok * 2} total conversation entries)")
if __name__ == "__main__":
main()
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