| """emo_data_prep.py v2 — R3 emotion-tagged training data. |
| Rows: "Speaker 0: <emotion> {transcription}" with audio = the acted clip (24 kHz) and |
| voice_prompts = [a DIFFERENT clip of the SAME actor+emotion] (list form, matching DailyTalk's |
| schema). Mixed with a DailyTalk sample (untagged) to prevent forgetting. |
| Outputs: /data/mixed_train.jsonl, /data/mixed_val.jsonl. Prints EMO PREP DONE. |
| """ |
| import json, os, random, sys |
| import numpy as np |
| import soundfile as sf |
| import librosa |
|
|
| SR = 24000 |
| TAG = {"ANGRY": "<angry>", "SAD": "<sad>", "HAPPY": "<happy>", |
| "NEUTRAL": "<neutral>", "DISGUST": "<disgust>", "FEARFUL": "<fearful>"} |
| CAP_PER_EMOTION = 250 |
| DT_SAMPLE = 300 |
| VAL_EMOTION = 45 |
| VAL_DT = 15 |
|
|
| random.seed(1234) |
|
|
|
|
| def main(): |
| from datasets import load_dataset |
| ds = load_dataset("zenitsu09/CREMAD-emotion-transcribed", split="train") |
| print(f"CREMA-D loaded: {len(ds)} rows", flush=True) |
|
|
| os.makedirs("/emo/audio", exist_ok=True) |
| rows = [] |
| count = {} |
| n_emo = 0 |
| for row in ds: |
| emo_raw = str(row["emotion"]).upper() |
| emo = "ANGRY" if emo_raw.startswith("ANG") else emo_raw |
| if emo not in TAG: |
| continue |
| if count.get(emo, 0) >= CAP_PER_EMOTION: |
| continue |
| count[emo] = count.get(emo, 0) + 1 |
| arr = np.asarray(row["audio"]["array"], dtype=np.float32) |
| sr = row["audio"]["sampling_rate"] |
| if sr != SR: |
| arr = librosa.resample(arr, orig_sr=sr, target_sr=SR) |
| text = str(row["transcription"]).strip() |
| if text.startswith("<"): |
| text = text.split(">", 1)[-1].strip() |
| if not text or len(text) < 4: |
| continue |
| n_emo += 1 |
| fn = f"/emo/audio/e{n_emo:05d}_{emo.lower()}.wav" |
| sf.write(fn, arr.astype(np.float32), SR) |
| actor = str(row["original_filename"]).split("_")[0] |
| rows.append({"text": f"Speaker 0: {TAG[emo]} {text}", "audio": fn, |
| "actor": actor, "emo": emo}) |
| print(f"emotion rows: {n_emo} ({count})", flush=True) |
|
|
| groups = {} |
| for r in rows: |
| groups.setdefault((r["actor"], r["emo"]), []).append(r) |
| paired = 0 |
| for r in rows: |
| pool = [x for x in groups.get((r["actor"], r["emo"]), []) if x is not r] |
| if pool: |
| r["voice_prompts"] = [random.choice(pool)["audio"]] |
| paired += 1 |
| else: |
| r["voice_prompts"] = [r["audio"]] |
| print(f"paired with different same-actor-same-emotion clip: {paired}/{n_emo}", flush=True) |
|
|
| dt = [json.loads(l) for l in open("/data/train.jsonl")] |
| random.shuffle(dt) |
| dt_sample = dt[:DT_SAMPLE] |
| print(f"dailytalk sample: {len(dt_sample)}", flush=True) |
| assert all(isinstance(r.get("voice_prompts"), list) for r in dt_sample[:5]), "dt schema check" |
|
|
| emo_rows = [{"text": r["text"], "audio": r["audio"], "voice_prompts": r["voice_prompts"]} |
| for r in rows] |
| random.shuffle(emo_rows) |
| val = emo_rows[:VAL_EMOTION] + dt_sample[:VAL_DT] |
| train = emo_rows[VAL_EMOTION:] + dt_sample[VAL_DT:] |
| random.shuffle(train) |
| random.shuffle(val) |
| with open("/data/mixed_train.jsonl", "w") as f: |
| for r in train: |
| f.write(json.dumps(r) + "\n") |
| with open("/data/mixed_val.jsonl", "w") as f: |
| for r in val: |
| f.write(json.dumps(r) + "\n") |
| print(f"MIXED TRAIN {len(train)} / VAL {len(val)}", flush=True) |
| print("EMO PREP DONE", flush=True) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
