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a/audio_1/music/v2_stableaudio/sfx/sfx_water_boiling_v1.mp3 b/audio_1/music/v2_stableaudio/sfx/sfx_water_boiling_v1.mp3 new file mode 100644 index 0000000..dfd4193 Binary files /dev/null and b/audio_1/music/v2_stableaudio/sfx/sfx_water_boiling_v1.mp3 differ diff --git a/audio_1/music/v2_stableaudio/sfx/sfx_water_boiling_v1.wav b/audio_1/music/v2_stableaudio/sfx/sfx_water_boiling_v1.wav new file mode 100644 index 0000000..a6dfd9e Binary files /dev/null and b/audio_1/music/v2_stableaudio/sfx/sfx_water_boiling_v1.wav differ diff --git a/audio_1/scripts/01_gen_tts_voxcpm2.py b/audio_1/scripts/01_gen_tts_voxcpm2.py new file mode 100644 index 0000000..abe6895 --- /dev/null +++ b/audio_1/scripts/01_gen_tts_voxcpm2.py @@ -0,0 +1,150 @@ +#!/usr/bin/env python3 +""" +步骤 1：TTS v1 — VoxCPM2 批量生成 +输出目录：audio_1/tts/v1_voxcpm2/ + +使用方法： + # 确认 VoxCPM2 服务已启动 + python audio_1/scripts/01_gen_tts_voxcpm2.py + python audio_1/scripts/01_gen_tts_voxcpm2.py --dry-run + python audio_1/scripts/01_gen_tts_voxcpm2.py --role linxia +""" +import os, sys, glob, base64, json, tempfile, time, subprocess, argparse +import requests + +PROJECT_DIR = "/home/xsl/blind" +VOICE_DIR = f"{PROJECT_DIR}/voice" +OUT_DIR = f"{PROJECT_DIR}/audio_1/tts/v1_voxcpm2" +HOST = "127.0.0.1" +PORT = "8000" +BASE_URL = f"http://{HOST}:{PORT}" + +os.makedirs(OUT_DIR, exist_ok=True) + +sys.path.insert(0, f"{PROJECT_DIR}/audio") +# 复用原始脚本的 ROLES 和 LINES 定义 +from batch_tts_voxcpm import ROLES, LINES + +# ────────────────────────────────────────────────────────────── +def find_ref_audio(role_key): + role_dir = os.path.join(VOICE_DIR, ROLES[role_key]["dir"]) + for ext in ("*.wav", "*.mp3", "*.flac"): + files = sorted(glob.glob(os.path.join(role_dir, ext))) + if files: + return files[0] + return None + +def wav_to_base64(path): + with open(path, "rb") as f: + return base64.b64encode(f.read()).decode() + +def convert_to_wav(src, dst): + subprocess.run(["ffmpeg", "-y", "-i", src, "-ar", "16000", "-ac", "1", + "-acodec", "pcm_s16le", dst], + check=True, capture_output=True) + +def apply_speed(src, speed, dst): + if abs(speed - 1.0) < 0.01: + import shutil; shutil.copy2(src, dst); return + subprocess.run(["ffmpeg", "-y", "-i", src, "-filter:a", f"atempo={speed}", + "-acodec", "pcm_s16le", dst], + check=True, capture_output=True) + +def wav_to_mp3(src, dst): + subprocess.run(["ffmpeg", "-y", "-i", src, "-codec:a", "libmp3lame", + "-b:a", "128k", "-ar", "24000", "-ac", "1", dst], + check=True, capture_output=True) + +def check_server(): + try: + return requests.get(f"{BASE_URL}/health", timeout=5).status_code == 200 + except: return False + +def register_voice(wav_path): + r = requests.post(f"{BASE_URL}/v1/voices", + json={"wav_base64": wav_to_base64(wav_path)}, timeout=60) + r.raise_for_status() + return r.json()["voice_id"] + +def synthesize(voice_id, text): + r = requests.post(f"{BASE_URL}/v1/speech", + json={"text": text, "voice_id": voice_id, + "cfg_value": 2.0, "inference_timesteps": 10}, + timeout=120) + r.raise_for_status() + return r.content + +# ────────────────────────────────────────────────────────────── +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--role", help="只合成指定角色") + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--resume", action="store_true", help="跳过已存在的文件", default=True) + args = parser.parse_args() + + lines = LINES + if args.role: + lines = [(f,r,t) for f,r,t in lines if r == args.role] + + print(f"\n{'='*60}") + print(f" TTS v1 — VoxCPM2 ({len(lines)} 条)") + print(f" 输出: {OUT_DIR}") + print(f"{'='*60}\n") + + if args.dry_run: + for f,r,t in lines: + out = os.path.join(OUT_DIR, f"{f}_wav_v1.mp3") + done = "✓" if os.path.exists(out) else "·" + print(f" [{done}] {f} [{r}] {t[:40]}") + return + + if not check_server(): + print(f"[错误] VoxCPM2 服务未响应: {BASE_URL}") + sys.exit(1) + + # 预注册音色 + voice_ids = {} + tmp = tempfile.mkdtemp(prefix="voxcpm_") + for role_key in set(r for _,r,_ in lines): + ref = find_ref_audio(role_key) + if not ref: + print(f" [跳过] {role_key}: 未找到参考音频"); continue + wav = ref if ref.endswith(".wav") else os.path.join(tmp, f"{role_key}.wav") + if not ref.endswith(".wav"): + convert_to_wav(ref, wav) + print(f" 注册音色: {role_key} ... ", end="", flush=True) + vid = register_voice(wav) + voice_ids[role_key] = vid + print(f"ok ({vid[:8]}...)") + + # 批量合成 + ok = fail = skip = 0 + for i, (fname, role, text) in enumerate(lines, 1): + out_mp3 = os.path.join(OUT_DIR, f"{fname}_wav_v1.mp3") + if args.resume and os.path.exists(out_mp3): + print(f" [{i:3d}/{len(lines)}] SKIP {fname}") + skip += 1; continue + + if role not in voice_ids: + print(f" [{i:3d}/{len(lines)}] MISS {fname} (无音色)") + fail += 1; continue + + speed = ROLES[role]["speed"] + print(f" [{i:3d}/{len(lines)}] {fname} text={text[:30]}... ", end="", flush=True) + t0 = time.time() + try: + wav_bytes = synthesize(voice_ids[role], text) + raw_wav = os.path.join(tmp, f"{fname}_raw.wav") + spd_wav = os.path.join(tmp, f"{fname}_spd.wav") + with open(raw_wav, "wb") as f: f.write(wav_bytes) + apply_speed(raw_wav, speed, spd_wav) + wav_to_mp3(spd_wav, out_mp3) + print(f"ok ({time.time()-t0:.1f}s {os.path.getsize(out_mp3)//1024}KB)") + ok += 1 + except Exception as e: + print(f"FAIL: {e}"); fail += 1 + + print(f"\n完成: {ok} ok, {skip} skip, {fail} fail → {OUT_DIR}") + +if __name__ == "__main__": + main() diff --git a/audio_1/scripts/02_gen_tts_cosyvoice2.py b/audio_1/scripts/02_gen_tts_cosyvoice2.py new file mode 100644 index 0000000..de86cfe --- /dev/null +++ b/audio_1/scripts/02_gen_tts_cosyvoice2.py @@ -0,0 +1,153 @@ +#!/usr/bin/env python3 +""" +步骤 2：TTS v2 — CosyVoice2-0.5B 批量生成 +输出目录：audio_1/tts/v2_cosyvoice2/ + + +使用方法： + conda activate cosyvoice + python audio_1/scripts/02_gen_tts_cosyvoice2.py + python audio_1/scripts/02_gen_tts_cosyvoice2.py --dry-run +""" +import os, sys, glob, json, time, tempfile, subprocess, argparse +import numpy as np + +PROJECT_DIR = "/home/xsl/blind" +VOICE_DIR = f"{PROJECT_DIR}/voice" +OUT_DIR = f"{PROJECT_DIR}/audio_1/tts/v2_cosyvoice2" +REF_TEXTS = f"{PROJECT_DIR}/audio_1/ref_texts.json" +MODEL_DIR = "/home/xsl/tools/CosyVoice/pretrained_models/CosyVoice2-0.5B" +COSYVOICE_DIR = "/home/xsl/tools/CosyVoice" + +os.makedirs(OUT_DIR, exist_ok=True) + +sys.path.insert(0, COSYVOICE_DIR) +sys.path.insert(0, f"{PROJECT_DIR}/audio") +from batch_tts_voxcpm import ROLES, LINES + +with open(REF_TEXTS, encoding="utf-8") as f: + REF_TEXT_MAP = json.load(f) + +# ────────────────────────────────────────────────────────────── +def find_ref_audio(role_key): + role_dir = os.path.join(VOICE_DIR, ROLES[role_key]["dir"]) + for ext in ("*.wav", "*.mp3", "*.flac"): + files = sorted(glob.glob(os.path.join(role_dir, ext))) + if files: + return files[0] + return None + +def load_audio_16k(path): + """加载音频，重采样到 16kHz，返回 numpy float32 array""" + import librosa + audio, _ = librosa.load(path, sr=16000, mono=True) + return audio + +def wav_to_mp3(src, dst): + subprocess.run(["ffmpeg", "-y", "-i", src, "-codec:a", "libmp3lame", + "-b:a", "128k", "-ar", "24000", "-ac", "1", dst], + check=True, capture_output=True) + +def save_wav(audio_tensor, sr, path): + """保存 torch tensor 或 numpy array 到 WAV""" + import soundfile as sf + if hasattr(audio_tensor, 'numpy'): + audio = audio_tensor.squeeze().numpy() + else: + audio = np.squeeze(audio_tensor) + sf.write(path, audio, sr) + +# ────────────────────────────────────────────────────────────── +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--role", help="只合成指定角色") + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--resume", action="store_true", default=True) + args = parser.parse_args() + + lines = LINES + if args.role: + lines = [(f,r,t) for f,r,t in lines if r == args.role] + + print(f"\n{'='*60}") + print(f" TTS v2 — CosyVoice2-0.5B ({len(lines)} 条)") + print(f" 输出: {OUT_DIR}") + print(f"{'='*60}\n") + + # 预检参考音频 + ref_cache = {} + for role_key in ROLES: + ref_path = find_ref_audio(role_key) + if ref_path: + ref_cache[role_key] = { + "path": ref_path, + "audio16k": None, # 懒加载 + "text": REF_TEXT_MAP.get(role_key, ""), + } + + if args.dry_run: + for f,r,t in lines: + out = os.path.join(OUT_DIR, f"{f}_wav_v1.mp3") + done = "✓" if os.path.exists(out) else "·" + ref_ok = "✓" if r in ref_cache else "✗" + print(f" [{done}] ref={ref_ok} {f} [{r}] {t[:40]}") + return + + # 加载模型 + print("加载 CosyVoice2 模型...", end=" ", flush=True) + t0 = time.time() + from cosyvoice.cli.cosyvoice import CosyVoice2 + cosyvoice = CosyVoice2(MODEL_DIR) + print(f"就绪 ({time.time()-t0:.1f}s) SR={cosyvoice.sample_rate}") + + tmp = tempfile.mkdtemp(prefix="cosyvoice2_") + ok = fail = skip = 0 + + for i, (fname, role, text) in enumerate(lines, 1): + out_mp3 = os.path.join(OUT_DIR, f"{fname}_wav_v1.mp3") + if args.resume and os.path.exists(out_mp3): + print(f" [{i:3d}/{len(lines)}] SKIP {fname}") + skip += 1; continue + + if role not in ref_cache: + print(f" [{i:3d}/{len(lines)}] MISS {fname} (无参考音频)") + fail += 1; continue + + speed = ROLES[role]["speed"] + ref = ref_cache[role] + print(f" [{i:3d}/{len(lines)}] {fname} [{role}] {text[:30]}... ", end="", flush=True) + t0 = time.time() + + try: + # 懒加载参考音频 + if ref["audio16k"] is None: + ref["audio16k"] = load_audio_16k(ref["path"]) + + # 合成：zero-shot 语音克隆 + import torch + prompt_speech = torch.from_numpy(ref["audio16k"]).unsqueeze(0) + audio_chunks = [] + for result in cosyvoice.inference_zero_shot( + tts_text=text, + prompt_text=ref["text"], + prompt_speech_16k=prompt_speech, + speed=speed, + stream=False, + ): + audio_chunks.append(result['tts_speech'].squeeze().numpy()) + + # 合并并保存 + full_audio = np.concatenate(audio_chunks) if audio_chunks else np.array([]) + out_wav = os.path.join(tmp, f"{fname}.wav") + import soundfile as sf + sf.write(out_wav, full_audio, cosyvoice.sample_rate) + wav_to_mp3(out_wav, out_mp3) + print(f"ok ({time.time()-t0:.1f}s {os.path.getsize(out_mp3)//1024}KB)") + ok += 1 + except Exception as e: + print(f"FAIL: {e}"); fail += 1 + + print(f"\n完成: {ok} ok, {skip} skip, {fail} fail → {OUT_DIR}") + +if __name__ == "__main__": + main() diff --git a/audio_1/scripts/04_gen_music_acestep.py b/audio_1/scripts/04_gen_music_acestep.py new file mode 100644 index 0000000..74162f7 --- /dev/null +++ b/audio_1/scripts/04_gen_music_acestep.py @@ -0,0 +1,122 @@ +#!/usr/bin/env python3 +""" +步骤 4：音乐 v1 — ACE-Step 1.5 批量生成 +输出目录：audio_1/music/v1_acestep/{music,sfx,ambience}/ + +使用方法： + conda activate acestep + python audio_1/scripts/04_gen_music_acestep.py [--dry-run] +""" +import os, sys, time, argparse, subprocess + +import torch +torch.backends.cuda.preferred_blas_library("cublaslt") + +PROJECT_DIR = "/home/xsl/blind" +REPO_DIR = "/home/xsl/tools/ACE-Step-1.5" +OUT_MUSIC = f"{PROJECT_DIR}/audio_1/music/v1_acestep/music" +OUT_SFX = f"{PROJECT_DIR}/audio_1/music/v1_acestep/sfx" +OUT_AMB = f"{PROJECT_DIR}/audio_1/music/v1_acestep/ambience" + +for d in (OUT_MUSIC, OUT_SFX, OUT_AMB): + os.makedirs(d, exist_ok=True) + +sys.path.insert(0, REPO_DIR) +sys.path.insert(0, f"{PROJECT_DIR}/audio") + +# 复用原脚本中的 TRACKS 和 SFX 定义 +from gen_music_acestep import BGM_TRACKS, RINGTONES +from gen_sfx_acestep import SFX_TRACKS, AMBIENCE_TRACKS + +def wav_to_mp3(src, dst): + subprocess.run(["ffmpeg", "-y", "-i", src, "-codec:a", "libmp3lame", + "-b:a", "128k", "-ar", "44100", "-ac", "2", dst], + check=True, capture_output=True) + +# ────────────────────────────────────────────────────────────── +def load_handler(): + from acestep.handler import AceStepHandler + print("加载 ACE-Step 模型...", end=" ", flush=True) + t0 = time.time() + handler = AceStepHandler() + handler.initialize_service(project_root=REPO_DIR, config_path="acestep-v15-turbo", device="cuda") + print(f"就绪 ({time.time()-t0:.1f}s)") + return handler + +def generate_one(handler, track, out_dir): + from acestep.inference import GenerationParams, GenerationConfig, generate_music + filename = track["filename"] + out_wav = os.path.join(out_dir, f"{filename}_v1.wav") + out_mp3 = os.path.join(out_dir, f"{filename}_v1.mp3") + + if os.path.exists(out_mp3): + print(f" [SKIP] {filename}") + return "skip" + + note = track.get("note", "") + print(f" [{filename}] {track['duration']}s {note} ...", end=" ", flush=True) + + params = GenerationParams( + caption=track["caption"], + lyrics=track.get("lyrics", ""), + duration=track["duration"], + instrumental=True, + inference_steps=track.get("steps", 20), + seed=track.get("seed", 42), + ) + config = GenerationConfig(batch_size=1, audio_format="wav") + t0 = time.time() + try: + result = generate_music(handler, None, params, config, save_dir=out_dir) + elapsed = time.time() - t0 + if result.success: + gen_path = result.audios[0]["path"] + if gen_path != out_wav: + os.rename(gen_path, out_wav) + wav_to_mp3(out_wav, out_mp3) + size = os.path.getsize(out_mp3) // 1024 + print(f"ok {elapsed:.1f}s {size}KB") + return "ok" + else: + print(f"FAIL: {result.error}"); return "fail" + except Exception as e: + print(f"FAIL: {e}"); return "fail" + +# ────────────────────────────────────────────────────────────── +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--category", choices=["music","sfx","ambience","all"], default="all") + args = parser.parse_args() + + all_tasks = [] + if args.category in ("music", "all"): + all_tasks += [(t, OUT_MUSIC) for t in BGM_TRACKS + RINGTONES] + if args.category in ("sfx", "all"): + all_tasks += [(t, OUT_SFX) for t in SFX_TRACKS] + if args.category in ("ambience", "all"): + all_tasks += [(t, OUT_AMB) for t in AMBIENCE_TRACKS] + + print(f"\n{'='*60}") + print(f" 音乐 v1 — ACE-Step ({len(all_tasks)} 条)") + print(f"{'='*60}\n") + + if args.dry_run: + for t, d in all_tasks: + mp3 = os.path.join(d, f"{t['filename']}_v1.mp3") + done = "✓" if os.path.exists(mp3) else "·" + print(f" [{done}] {t['filename']} {t['duration']}s → {os.path.basename(d)}/") + return + + handler = load_handler() + ok = fail = skip = 0 + for track, out_dir in all_tasks: + r = generate_one(handler, track, out_dir) + if r == "ok": ok += 1 + elif r == "skip": skip += 1 + else: fail += 1 + + print(f"\n完成: {ok} ok, {skip} skip, {fail} fail") + +if __name__ == "__main__": + main() diff --git a/audio_1/scripts/05_gen_music_stableaudio.py b/audio_1/scripts/05_gen_music_stableaudio.py new file mode 100644 index 0000000..da57918 --- /dev/null +++ b/audio_1/scripts/05_gen_music_stableaudio.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python3 +""" +步骤 5：音乐 v2 — Stable Audio Open 1.0 批量生成 +输出目录：audio_1/music/v2_stableaudio/{music,sfx,ambience}/ + +特点：44.1kHz 立体声，最长 47 秒，音效/氛围质量高，BGM 风格偏弥散 + +使用方法： + conda activate stableaudio + python audio_1/scripts/05_gen_music_stableaudio.py [--dry-run] +""" +import os, sys, time, subprocess, argparse +import torch, json + +PROJECT_DIR = "/home/xsl/blind" +TOOLS_DIR = "/home/xsl/tools/stable-audio-tools" +MODEL_CONFIG = "/home/xsl/models/stable-audio-open/model_config.json" +MODEL_CKPT = "/home/xsl/models/stable-audio-open/model.safetensors" +OUT_MUSIC = f"{PROJECT_DIR}/audio_1/music/v2_stableaudio/music" +OUT_SFX = f"{PROJECT_DIR}/audio_1/music/v2_stableaudio/sfx" +OUT_AMB = f"{PROJECT_DIR}/audio_1/music/v2_stableaudio/ambience" + +for d in (OUT_MUSIC, OUT_SFX, OUT_AMB): + os.makedirs(d, exist_ok=True) + +sys.path.insert(0, TOOLS_DIR) + +# ── 音乐 BGM（Stable Audio 最长 47s，生成片段）───────────────── +BGM_TRACKS = [ + { + "filename": "lv11_bgm_m1_main_loop", + "duration": 47, + "prompt": ( + "lo-fi midnight bedroom, soft piano, light vinyl crackle, " + "muted piano, subtle synth pad, occasional distant rain, " + "melancholy but warm, alone but not lonely, ambient, no drums, 70 bpm" + ), + "steps": 200, + "seed": 2103, + "note": "M1 主题", + }, + { + "filename": "lv11_bgm_m2_suspense", + "duration": 30, + "prompt": ( + "subtle ambient suspense, cinematic tension, " + "low drone, single hanging piano note, sparse strings, " + "unsettling but not horror, builds slowly, unresolved chord, 60 bpm" + ), + "steps": 200, + "seed": 2230, + "note": "M2 悬疑", + }, + { + "filename": "lv11_bgm_m3_zhounan", + "duration": 47, + "prompt": ( + "late night lo-fi, warm electric guitar fingerpicking, " + "light reverb, distant city sounds, nostalgic, gentle, 65 bpm" + ), + "steps": 200, + "seed": 2345, + "note": "M3 周南", + }, + { + "filename": "lv11_bgm_m4_ending", + "duration": 47, + "prompt": ( + "gentle piano dawn, morning light, hopeful, sparse chords, " + "soft string pad, peaceful resolution, 60 bpm" + ), + "steps": 200, + "seed": 2400, + "note": "M4 结局", + }, +] + +# ── 铃声（短促，~10-15s）──────────────────────────────────────── +RINGTONES = [ + {"filename": "lv11_ring_mom", "duration": 12, "seed": 3001, "steps": 150, + "prompt": "warm marimba ringtone, simple 4-note motif, old Nokia style, 70 bpm, loop, short"}, + {"filename": "lv11_ring_azhe", "duration": 12, "seed": 3002, "steps": 150, + "prompt": "cold minimal electronic ringtone, 2 notes only, distant, 80 bpm, loop, short"}, + {"filename": "lv11_ring_xiaomei", "duration": 12, "seed": 3003, "steps": 150, + "prompt": "cheerful pop ringtone, ukulele plucks, 4-bar phrase, energetic, 100 bpm, loop"}, + {"filename": "lv11_ring_zhounan", "duration": 12, "seed": 3004, "steps": 150, + "prompt": "acoustic guitar single-string ringtone, contemplative, 75 bpm, loop, short"}, + {"filename": "lv11_ring_hr", "duration": 12, "seed": 3005, "steps": 150, + "prompt": "corporate professional ringtone, neutral marimba, clean, 90 bpm, loop"}, + {"filename": "lv11_ring_anan", "duration": 12, "seed": 3006, "steps": 150, + "prompt": "fun upbeat ringtone, synth pop, bright, 110 bpm, catchy, loop"}, + {"filename": "lv11_ring_mom", "duration": 12, "seed": 3007, "steps": 150, + "prompt": "delivery notification ringtone, simple beep melody, neutral, 85 bpm"}, + {"filename": "lv11_ring_delivery", "duration": 12, "seed": 3008, "steps": 150, + "prompt": "delivery notification ringtone, simple beep melody, neutral, 85 bpm"}, + {"filename": "lv11_ring_unknown", "duration": 12, "seed": 3009, "steps": 150, + "prompt": "eerie unknown caller ringtone, distorted sine wave, unsettling, 60 bpm"}, + {"filename": "lv11_ring_dorm_group","duration": 12, "seed": 3010, "steps": 150, + "prompt": "group chat notification sound, light percussion, cheerful, 95 bpm, loop"}, + {"filename": "lv11_ring_zhounan", "duration": 12, "seed": 3011, "steps": 150, + "prompt": "acoustic guitar single-string ringtone, contemplative, 75 bpm, loop"}, +] + +# ── 音效 SFX ─────────────────────────────────────────────────── +SFX_TRACKS = [ + {"filename": "sfx_transition_chapter", "duration": 8, "seed": 4001, "steps": 200, + "prompt": "cinematic transition swell, soft orchestral rise and fall, gentle fade, film score"}, + {"filename": "sfx_heartbeat_fast", "duration": 8, "seed": 4002, "steps": 200, + "prompt": "human heartbeat accelerating, close microphone, thumping, tension, anxiety, realistic"}, + {"filename": "sfx_deep_breath", "duration": 6, "seed": 4003, "steps": 200, + "prompt": "single deep breath, calm, close microphone, soft exhale, meditative, realistic foley"}, + {"filename": "sfx_rain_heavier", "duration": 8, "seed": 4004, "steps": 200, + "prompt": "rain suddenly intensifying on window glass, urban rain sound, dramatic, realistic"}, + {"filename": "sfx_rain_lighter", "duration": 8, "seed": 4005, "steps": 200, + "prompt": "light rain on window, soft pattering, urban night, gentle, calming, realistic"}, + {"filename": "sfx_glass_shatter_short","duration": 5, "seed": 4006, "steps": 200, + "prompt": "glass breaking, short, sharp, immediate, foley sound effect"}, + {"filename": "sfx_door_knock_gentle", "duration": 5, "seed": 4007, "steps": 200, + "prompt": "gentle knocking on door, two light knocks, quiet, hesitant, foley"}, + {"filename": "sfx_door_knock_firm", "duration": 5, "seed": 4008, "steps": 200, + "prompt": "firm confident door knock, three knocks, hollow door resonance, foley"}, + {"filename": "sfx_phone_vibrate", "duration": 5, "seed": 4009, "steps": 200, + "prompt": "smartphone vibrating on hard surface, buzzing notification, realistic"}, + {"filename": "sfx_keyboard_fast", "duration": 8, "seed": 4010, "steps": 200, + "prompt": "fast typing on mechanical keyboard, rapid keystrokes, focused, realistic"}, + {"filename": "sfx_keyboard_slow", "duration": 8, "seed": 4011, "steps": 200, + "prompt": "slow hesitant typing, single key presses, pauses, uncertain typing, realistic"}, + {"filename": "sfx_kettle_whistle", "duration": 8, "seed": 4012, "steps": 200, + "prompt": "electric kettle boiling and whistling, kitchen, steam, morning, realistic foley"}, + {"filename": "sfx_water_boiling", "duration": 8, "seed": 4013, "steps": 200, + "prompt": "water boiling in pot, bubbling, kitchen ambience, realistic foley sound"}, + {"filename": "sfx_paper_turning", "duration": 5, "seed": 4014, "steps": 200, + "prompt": "paper page turning, subtle rustling, quiet, realistic close-mic foley"}, + {"filename": "sfx_night_bus", "duration": 10, "seed": 4015, "steps": 200, + "prompt": "night bus interior sound, engine rumble, gentle sway, city at night, ambient"}, + {"filename": "sfx_cat_yowl", "duration": 5, "seed": 4016, "steps": 200, + "prompt": "cat yowling outside at night, distant, urban night sound, realistic"}, + {"filename": "sfx_ending_chime", "duration": 6, "seed": 4017, "steps": 200, + "prompt": "soft ending chime, single bell tone, resonant, gentle, conclusive, musical"}, + {"filename": "sfx_breathing_unknown", "duration": 8, "seed": 4018, "steps": 200, + "prompt": "quiet breathing, slightly nervous, close microphone, intimate, human, realistic"}, + {"filename": "sfx_deep_breath", "duration": 6, "seed": 4019, "steps": 200, + "prompt": "slow deep calming breath, inhale and exhale, meditation, close mic, peaceful"}, +] + +# ── 环境氛围 ────────────────────────────────────────────────── +AMBIENCE_TRACKS = [ + {"filename": "amb_apartment_night_01", "duration": 47, "seed": 5001, "steps": 200, + "prompt": "apartment at night, city sounds, distant traffic, quiet hum, urban isolation, ambient"}, + {"filename": "amb_apartment_night_02", "duration": 47, "seed": 5002, "steps": 200, + "prompt": "late night apartment, occasional car passing, silence, urban night, subtle AC hum"}, + {"filename": "amb_bar_loud_01", "duration": 30, "seed": 5003, "steps": 200, + "prompt": "busy bar ambience, crowd chatter, glasses clinking, background music, lively, indoor"}, + {"filename": "amb_rain_window_01", "duration": 47, "seed": 5004, "steps": 200, + "prompt": "rain on window glass, cozy indoor, distant thunder rumble, peaceful, continuous"}, + {"filename": "amb_kitchen_morning", "duration": 30, "seed": 5005, "steps": 200, + "prompt": "morning kitchen sounds, birds outside, soft light, gentle hum, peaceful domestic"}, + {"filename": "amb_dawn_birds", "duration": 47, "seed": 5006, "steps": 200, + "prompt": "dawn birdsong, early morning outdoor, multiple bird species, peaceful, hopeful"}, +] + +# ────────────────────────────────────────────────────────────── +def wav_to_mp3(src, dst): + subprocess.run(["ffmpeg", "-y", "-i", src, "-codec:a", "libmp3lame", + "-b:a", "128k", "-ar", "44100", "-ac", "2", dst], + check=True, capture_output=True) + +def generate_one(track, out_dir, sa_model, sr): + """使用 generate_diffusion_cond 直接生成音频""" + import numpy as np + from einops import rearrange + from stable_audio_tools.inference.generation import generate_diffusion_cond + import torchaudio + + filename = track["filename"] + out_wav = os.path.join(out_dir, f"{filename}_v1.wav") + out_mp3 = os.path.join(out_dir, f"{filename}_v1.mp3") + + if os.path.exists(out_mp3): + print(f" [SKIP] {filename}") + return "skip" + + note = track.get("note", "") + duration = min(track["duration"], 47) + print(f" [{filename}] {duration}s {note}... ", end="", flush=True) + t0 = time.time() + + try: + device = next(sa_model.parameters()).device + conditioning = [{"prompt": track["prompt"], "seconds_start": 0, "seconds_total": duration}] + audio = generate_diffusion_cond( + model=sa_model, + conditioning=conditioning, + steps=track.get("steps", 200), + cfg_scale=7.0, + batch_size=1, + sample_size=sr * duration, + seed=track.get("seed", 42), + device=device, + sampler_type="dpmpp-3m-sde", + sigma_min=0.03, + sigma_max=1000, + ) + # audio shape: [batch, channels, samples] + audio = audio[:, :, :duration * sr] + audio = rearrange(audio, "b d n -> d (b n)") + audio = audio.to(torch.float32) + peak = torch.max(torch.abs(audio)) + if peak > 0: + audio = audio / peak + audio = audio.clamp(-1, 1).mul(32767).to(torch.int16).cpu() + torchaudio.save(out_wav, audio, sr) + wav_to_mp3(out_wav, out_mp3) + size = os.path.getsize(out_mp3) // 1024 + print(f"ok {time.time()-t0:.1f}s {size}KB") + return "ok" + except Exception as e: + import traceback; traceback.print_exc() + print(f"FAIL: {e}"); return "fail" + +# ────────────────────────────────────────────────────────────── +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--category", choices=["music","sfx","ambience","all"], default="all") + args = parser.parse_args() + + all_tasks = [] + if args.category in ("music", "all"): + all_tasks += [(t, OUT_MUSIC) for t in BGM_TRACKS + RINGTONES] + if args.category in ("sfx", "all"): + all_tasks += [(t, OUT_SFX) for t in SFX_TRACKS] + if args.category in ("ambience", "all"): + all_tasks += [(t, OUT_AMB) for t in AMBIENCE_TRACKS] + + # 去重（RINGTONES 里有重复 filename） + seen = set() + deduped = [] + for t, d in all_tasks: + key = (t["filename"], d) + if key not in seen: + seen.add(key) + deduped.append((t, d)) + all_tasks = deduped + + print(f"\n{'='*60}") + print(f" 音乐 v2 — Stable Audio Open 1.0 ({len(all_tasks)} 条)") + print(f"{'='*60}\n") + + if args.dry_run: + for t, d in all_tasks: + mp3 = os.path.join(d, f"{t['filename']}_v1.mp3") + done = "✓" if os.path.exists(mp3) else "·" + print(f" [{done}] {t['filename']} {t['duration']}s → {os.path.basename(d)}/") + return + + # 加载模型 + print(f"加载 Stable Audio 模型...", end=" ", flush=True) + t0 = time.time() + from stable_audio_tools.interface.gradio import load_model as sa_load_model + import json + with open(MODEL_CONFIG) as f: + model_config = json.load(f) + sa_model, _ = sa_load_model(model_config=model_config, model_ckpt_path=MODEL_CKPT, device="cuda", model_half=False) + print(f"就绪 ({time.time()-t0:.1f}s)") + + ok = fail = skip = 0 + for track, out_dir in all_tasks: + r = generate_one(track, out_dir, sa_model, model_config["sample_rate"]) + if r == "ok": ok += 1 + elif r == "skip": skip += 1 + else: fail += 1 + + print(f"\n完成: {ok} ok, {skip} skip, {fail} fail") + +if __name__ == "__main__": + main() diff --git a/audio_1/scripts/06_convert_all_to_mp3.py b/audio_1/scripts/06_convert_all_to_mp3.py new file mode 100644 index 0000000..68af17e --- /dev/null +++ b/audio_1/scripts/06_convert_all_to_mp3.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 +""" +步骤 6：格式整理 — 将 audio_1 目录下所有残留 WAV 转换为 MP3，并清理中间文件 +输出：各子目录内的 *_v1.mp3 + +使用方法： + python audio_1/scripts/06_convert_all_to_mp3.py [--dry-run] [--clean-wav] +""" +import os, subprocess, argparse + +AUDIO_1_DIR = "/home/xsl/blind/audio_1" + +def wav_to_mp3(src, dst): + subprocess.run(["ffmpeg", "-y", "-i", src, "-codec:a", "libmp3lame", + "-b:a", "128k", "-ar", "44100", "-ac", "2", dst], + check=True, capture_output=True) + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--dry-run", action="store_true", help="只列出，不执行") + parser.add_argument("--clean-wav", action="store_true", help="转换后删除 WAV 源文件") + args = parser.parse_args() + + wavs_found = [] + for root, dirs, files in os.walk(AUDIO_1_DIR): + # 跳过 scripts 目录 + if "scripts" in root: + continue + for fname in files: + if fname.endswith(".wav"): + wav_path = os.path.join(root, fname) + mp3_path = wav_path.replace(".wav", ".mp3") + wavs_found.append((wav_path, mp3_path)) + + if not wavs_found: + print("无需转换的 WAV 文件。") + return + + print(f"\n找到 {len(wavs_found)} 个 WAV 文件\n") + ok = skip = fail = 0 + for wav, mp3 in wavs_found: + rel = os.path.relpath(wav, AUDIO_1_DIR) + if os.path.exists(mp3): + print(f" [SKIP] {rel}") + skip += 1 + continue + if args.dry_run: + print(f" [DRY] {rel} → {os.path.basename(mp3)}") + continue + try: + wav_to_mp3(wav, mp3) + size = os.path.getsize(mp3) // 1024 + print(f" [OK] {rel} → {size}KB") + if args.clean_wav: + os.remove(wav) + ok += 1 + except Exception as e: + print(f" [FAIL] {rel}: {e}") + fail += 1 + + print(f"\n完成: {ok} ok, {skip} skip, {fail} fail") + + # 统计各目录 MP3 数量 + print("\n─── 音频资源统计 ───") + subdirs = [ + ("tts/v1_voxcpm2", "TTS v1 VoxCPM2"), + ("music/v1_acestep/music", "Music v1 ACE-Step BGM"), + ("music/v1_acestep/sfx", "Music v1 ACE-Step SFX"), + ("music/v1_acestep/ambience", "Music v1 ACE-Step Amb"), + ("music/v2_stableaudio/music", "Music v2 StableAudio BGM"), + ("music/v2_stableaudio/sfx", "Music v2 StableAudio SFX"), + ("music/v2_stableaudio/ambience", "Music v2 StableAudio Amb"), + ] + for rel_dir, label in subdirs: + full = os.path.join(AUDIO_1_DIR, rel_dir) + if not os.path.isdir(full): + count = 0 + else: + count = len([f for f in os.listdir(full) if f.endswith(".mp3")]) + print(f" {label:<30} {count:>4} 条") + +if __name__ == "__main__": + main() diff --git a/audio_1/tts/v1_voxcpm2/lv11_0015_mom_call_01_wav_v1.mp3 b/audio_1/tts/v1_voxcpm2/lv11_0015_mom_call_01_wav_v1.mp3 new file mode 100644 index 0000000..e28d103 Binary files /dev/null and b/audio_1/tts/v1_voxcpm2/lv11_0015_mom_call_01_wav_v1.mp3 differ diff --git a/audio_1/tts/v1_voxcpm2/lv11_0200_mom_voice_01_seg1_wav_v1.mp3 b/audio_1/tts/v1_voxcpm2/lv11_0200_mom_voice_01_seg1_wav_v1.mp3 new file mode 100644 index 0000000..a696f29 Binary files /dev/null and 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b/audio_1/tts/v2_cosyvoice2/v2_sys_paused_wav_v1.mp3 differ diff --git a/audio_1/tts/v2_cosyvoice2/v2_sys_wait_reply_wav_v1.mp3 b/audio_1/tts/v2_cosyvoice2/v2_sys_wait_reply_wav_v1.mp3 new file mode 100644 index 0000000..f3669ca Binary files /dev/null and b/audio_1/tts/v2_cosyvoice2/v2_sys_wait_reply_wav_v1.mp3 differ diff --git a/audio_1/执行文档.md b/audio_1/执行文档.md new file mode 100644 index 0000000..135e0be --- /dev/null +++ b/audio_1/执行文档.md @@ -0,0 +1,169 @@ +# 《林夏》音频资源重制执行文档 + +> 创建日期：2026-05-20 +> 目标：独立生成一套 TTS 语音 + 两套音乐音效，供对比选择 +> 游戏代码不做任何修改，audio_1/ 仅用于评听比较 + +--- + +## 一、目录结构 + +``` +audio_1/ +├── 执行文档.md ← 本文件 +├── tts/ +│ └── v1_voxcpm2/ TTS版本1：VoxCPM2（原方案复刻） +└── music/ + ├── v1_acestep/ 音乐版本1：ACE-Step 1.5（原方案复刻） + │ ├── music/ + │ ├── sfx/ + │ └── ambience/ + └── v2_stableaudio/ 音乐版本2：Stable Audio Open 1.0 + ├── music/ + ├── sfx/ + └── ambience/ +``` + +**文件命名规则：** 与 `audio/mp3/tts/` 完全相同（如 `v2_msg02_linxia_warm_wav_v1.mp3`） +→ 若要上游切换到某版本，只需将对应目录复制覆盖 `audio/mp3/tts/` 即可 + +--- + +## 二、资源清单 + +| 类别 | 文件数 | 说明 | +|------|--------|------| +| TTS — NPC 来电语音 | ~30 | lv11_* 开头，妈妈/小美/外卖/阿哲等11个角色 | +| TTS — 林夏回应 | ~90 | v2_msg*_linxia_* | +| TTS — NPC 反应 | ~60 | v2_msg*_react_* | +| TTS — 系统提示 | 4 | v2_sys_* | +| TTS — 结局旁白 | 7 | v2_ending_* | +| **TTS 合计** | **~190** | | +| 音乐 BGM | 4 | lv11_bgm_m1~m4 | +| 铃声 | 9 | lv11_ring_* | +| 音效 SFX | 18 | sfx_* | +| 环境氛围 | 6 | amb_* | +| **音乐合计/版本** | **37** | × 2 版本 = 74个文件 | + +--- + +## 三、技术方案 + +### 音乐工具对比 + +| 项目 | v1 ACE-Step | v2 Stable Audio | +|------|------------|-----------------| +| conda 环境 | acestep | stableaudio | +| 优势 | 已验证效果，可控 seed | 44.1kHz 立体声，音效更真实 | +| 劣势 | 音乐结构感强，SFX 偏长 | 不擅长节奏明确的循环 BGM | +| 最适合 | BGM + 铃声 | SFX + 环境氛围 | + +--- + +## 四、执行步骤 + +### 步骤 0：准备工作（一次性） + +```bash +# 创建目录 +mkdir -p /home/xsl/blind/audio_1/tts/v1_voxcpm2 +mkdir -p /home/xsl/blind/audio_1/music/v1_acestep/{music,sfx,ambience} +mkdir -p /home/xsl/blind/audio_1/music/v2_stableaudio/{music,sfx,ambience} +``` + +### 步骤 1：TTS v1 — VoxCPM2 + +```bash +# 确认服务运行 +curl http://127.0.0.1:8000/health + +# 执行批量生成（输出到 audio_1/tts/v1_voxcpm2/） +python audio_1/scripts/01_gen_tts_voxcpm2.py +``` + +**预计耗时**：约 90 分钟（~190条 × ~30s/条） + +### 步骤 2：音乐 v1 — ACE-Step + +```bash +conda activate acestep +python audio_1/scripts/04_gen_music_acestep.py +``` + +**预计耗时**：约 60 分钟（37 条，每条 20~90s） + +### 步骤 3：音乐 v2 — Stable Audio + +```bash +conda activate stableaudio +python audio_1/scripts/05_gen_music_stableaudio.py +``` + +**预计耗时**：约 90 分钟（37 条，每条 ~2min） + +### 步骤 4：格式统一 + +```bash +# 所有版本统一转为 mp3 128kbps 24kHz mono +python audio_1/scripts/06_convert_all_to_mp3.py +``` + +--- + +## 五、脚本清单 + +| 脚本 | 职责 | +|------|------| +| `scripts/01_gen_tts_voxcpm2.py` | VoxCPM2 批量 TTS，输出到 tts/v1_voxcpm2/ | +| `scripts/04_gen_music_acestep.py` | ACE-Step 批量生成，输出到 music/v1_acestep/ | +| `scripts/05_gen_music_stableaudio.py` | Stable Audio 批量生成，输出到 music/v2_stableaudio/ | +| `scripts/06_convert_all_to_mp3.py` | WAV → MP3 统一格式转换 | + +--- + +## 六、角色参考音频路径 + +| 角色 key | 参考音频 | 语速倍率 | +|----------|---------|---------| +| linxia | voice/linxia/linxia_ref.wav | 1.00 | +| mom | voice/mom/mom_extra_04.wav | 0.92 | +| azhe | voice/azhe/azhe_extra_05.wav | 1.05 | +| xiaomei | voice/xiaomei/xiaomei_03.wav | 1.10 | +| xiaomei_drunk | voice/xiaomei/xiaomei_03.wav | 0.95 | +| zhounan | voice/zhounan/zhounan_extra_10.wav | 0.95 | +| hr | voice/hr/hr_extra_18.wav | 1.00 | +| anan | voice/anan/anan_extra_13.wav | 1.15 | +| dorm_a | voice/dorm_a/000017e3-...wav | 1.10 | +| dorm_b | voice/dorm_b/a001c446-...wav | 1.00 | +| dorm_c | voice/dorm_c/dorm_c_extra_15.wav | 0.95 | +| delivery | voice/delivery/delivery_01.wav | 1.20 | +| self_3y | voice/linxia/linxia_ref.wav | 1.15 | + +--- + +## 七、评听方式（完成后） + +所有文件名与游戏引用完全一致。评听时可临时替换： + +```bash +# 备份原版本 +cp -r audio/mp3/tts audio/mp3/tts_original + +# 试听某个 TTS 版本 +cp -r audio_1/tts/v1_voxcpm2/* audio/mp3/tts/ + +# 试听音乐版本 +cp -r audio_1/music/v2_stableaudio/* audio/mp3/ +``` + +--- + +## 八、进度记录 + +| 步骤 | 状态 | 完成时间 | 备注 | +|------|------|---------|------| +| 步骤 0：准备（创建目录） | ⬜ 待执行 | — | — | +| 步骤 1：TTS v1 VoxCPM2 | ⬜ 待执行 | — | — | +| 步骤 2：音乐 v1 ACE-Step | ⬜ 待执行 | — | — | +| 步骤 3：音乐 v2 Stable Audio | ⬜ 待执行 | — | — | +| 步骤 4：格式转换 | ⬜ 待执行 | — | — | diff --git a/技术报告_音频资源制作.md b/技术报告_音频资源制作.md index 00b7be7..021f295 100644 --- a/技术报告_音频资源制作.md +++ b/技术报告_音频资源制作.md @@ -1,7 +1,7 @@ # 音频资源制作技术落地报告 > 调研日期：2026-05-20 -> 最后更新：2026-05-20（新增 GPT-SoVITS / Fish Speech / Stable Audio 部署完成状态） +> 最后更新：2026-05-20（新增 Stable Audio 部署完成状态） > 硬件环境：NVIDIA RTX 5090 (32GB VRAM) / CUDA Driver 12.9 / WSL2 Linux --- @@ -12,8 +12,6 @@ |--------|------|------|------| | TTS 语音合成 | VoxCPM2 | **运行中** (port 8000) | Python 3.13 venv | | TTS 语音合成 | CosyVoice2-0.5B | 已部署，需手动启动 | conda cosyvoice | -| TTS 语音合成 | **GPT-SoVITS v2** | **已部署** ✅ | conda gptsovits / torch 2.8.0+cu128 | -| TTS 语音合成 | **Fish Speech 2.0** | **已部署** ✅ | conda fishspeech / torch 2.8.0+cu128 | | 音乐/铃声生成 | ACE-Step 1.5 | 已部署，需手动启动 | conda acestep | | 音效/氛围生成 | ACE-Step 1.5 | 同上 | conda acestep | | 音效/氛围生成 | **Stable Audio Open 1.0** | **已部署** ✅ | conda stableaudio / torch 2.8.0+cu128 | @@ -56,15 +54,11 @@ | 工具 | Stars | 模型大小 | VRAM | 为什么值得部署 | 部署难度 | |------|-------|---------|------|--------------|---------| -| **GPT-SoVITS v2** | 57k | ~3GB | 4-6GB | 中文 TTS 天花板；1 分钟参考音频即可克隆；自带 ASR 标注工具链；MIT 开源 | 中 (conda + WebUI) | -| **Fish Speech S2** | 26k | ~4GB | 4-24GB | 10M+小时训练数据；15000 种情感标签；WER 最低；接近 ElevenLabs 品质 | 中 (Docker 或 pip) | | **F5-TTS** | 14.5k | ~3GB | 8GB | Flow-matching 架构；零样本克隆质量最高；MIT 开源 | 低 (pip install) | | **ChatTTS** | 39k | ~2GB | 4GB | 专为对话设计；自然笑声/停顿/语气词；适合互动叙事 | 低 (pip install) | **部署优先级建议**： -1. **GPT-SoVITS** — 综合最强，完整工具链（ASR标注 → 训练 → 推理），适合为《林夏》每个角色精调专属声线 -2. **Fish Speech S2** — 情感控制力最强，适合需要微妙情绪变化的场景（醉酒小美、哽咽妈妈） -3. **ChatTTS** — 对话自然度最高，适合即兴/随机对白生成 +1. **ChatTTS** — 对话自然度最高，适合即兴/随机对白生成 #### 线上服务 @@ -164,12 +158,11 @@ voice/ | 工具 | Stars | 为什么值得 | 适用场景 | |------|-------|----------|---------| -| **GPT-SoVITS** | 57k | 1 分钟参考即可高保真克隆；内置 ASR+标注+训练全链路；RTF 0.014 (4090) | 为角色训练专属声线模型 | | **OpenVoice V2** | 36k | 单条音频秒级克隆；跨语言（克隆英文声 → 输出中文）| 快速原型、临时角色 | | **RVC v2** | 36k | 实时变声；10 分钟数据可训练；社区模型库庞大 | 歌声转换、直播变声 | | **Seed-VC** | 新 | 零样本相似度最高；解决音色泄漏问题；支持唱歌 | 高精度声音复刻 | -**部署优先级**：GPT-SoVITS > Seed-VC > OpenVoice V2 +**部署优先级**：Seed-VC > OpenVoice V2 --- @@ -189,8 +182,6 @@ voice/ | 序号 | 工具 | 路径 | 模型 | torch | |------|------|------|------|-------| | 1 | **Stable Audio Open 1.0** | `/home/xsl/tools/stable-audio-tools/` | `/home/xsl/models/stable-audio-open/` | 2.8.0+cu128 (stableaudio) | -| 2 | **GPT-SoVITS v2** | `/home/xsl/tools/GPT-SoVITS/` | `GPT_SoVITS/pretrained_models/` (含 v2Pro/v3) | 2.8.0+cu128 (gptsovits) | -| 3 | **Fish Speech 2.0** | `/home/xsl/tools/fish-speech/` | `/home/xsl/models/fish-speech-1.5/` | 2.8.0+cu128 (fishspeech) | **注：全部使用 CUDA 12.8 编译版 torch，支持 RTX 5090 (sm_120)**。 PyTorch wheel 来源：`https://mirrors.aliyun.com/pytorch-wheels/cu128/` @@ -204,23 +195,6 @@ cd /home/xsl/tools/stable-audio-tools python run_gradio.py \ --model-config /home/xsl/models/stable-audio-open/model_config.json \ --ckpt-path /home/xsl/models/stable-audio-open/model.safetensors - -# GPT-SoVITS — WebUI（含 ASR 标注 + 训练 + 推理） -conda activate gptsovits -cd /home/xsl/tools/GPT-SoVITS -python webui.py -# 默认 http://127.0.0.1:9872 - -# Fish Speech — API 服务 -conda activate fishspeech -cd /home/xsl/tools/fish-speech -python -m tools.api \ - --listen 0.0.0.0:8080 \ - --llama-checkpoint-dir /home/xsl/models/fish-speech-1.5 \ - --decoder-checkpoint-dir /home/xsl/models/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth \ - --decoder-config-name firefly_gan_vq -# 或 Gradio WebUI: -python tools/run_webui.py --llama-checkpoint-dir /home/xsl/models/fish-speech-1.5 ``` ### 下一优先级 — 待部署 @@ -247,9 +221,8 @@ python tools/run_webui.py --llama-checkpoint-dir /home/xsl/models/fish-speech-1. ┌───────────────┐ ┌────────────┐ ┌────────────┐ TTS │ VoxCPM2 │ │ ChatTTS │ │ MiniMax │ 语音合成 │ CosyVoice2 │ │ F5-TTS │ │ 豆包 TTS │ - │ GPT-SoVITS ✅ │ │ │ │ ElevenLabs │ - │ Fish Speech ✅│ │ │ │ 通义 TTS │ - │ Kokoro-82M │ │ │ │ │ + │ Kokoro-82M │ │ │ │ ElevenLabs │ + │ │ │ │ │ 通义 TTS │ └───────────────┘ └────────────┘ └────────────┘ ┌───────────────┐ ┌────────────┐ ┌────────────┐ 音乐生成 │ ACE-Step │ │ DiffRhythm │ │ Suno v5 │ @@ -265,8 +238,7 @@ python tools/run_webui.py --llama-checkpoint-dir /home/xsl/models/fish-speech-1. ┌───────────────┐ ┌────────────┐ ┌────────────┐ 声音克隆 │ VoxCPM2 │ │ Seed-VC │ │ ElevenLabs │ │ CosyVoice2 │ │ OpenVoice │ │ MiniMax │ - │ GPT-SoVITS ✅ │ │ RVC v2 │ │ 豆包 │ - │ Fish Speech ✅│ │ │ │ │ + │ │ │ RVC v2 │ │ 豆包 │ └───────────────┘ └────────────┘ └────────────┘ ┌───────────────┐ ASR │ SenseVoice │ (辅助能力，非生产主线) @@ -284,7 +256,6 @@ RTX 5090 (32GB) 可同时运行的组合： |------|----------|------| | VoxCPM2 (6G) + ACE-Step (8G) | ~14GB | TTS + 音乐同时生产 | | VoxCPM2 (6G) + Stable Audio (12G) | ~18GB | TTS + 高品质音效 | -| GPT-SoVITS (6G) + ACE-Step (8G) | ~14GB | 高品质 TTS + 音乐 | | CosyVoice2 (8G) + ChatTTS (4G) | ~12GB | 双 TTS 引擎对比试听 | | 单独 YuE 量化版 | ~8-24GB | 完整歌曲生成（独占） | @@ -294,7 +265,5 @@ RTX 5090 (32GB) 可同时运行的组合： | 现状 | 升级方案 | 收益 | |------|---------|------| -| VoxCPM2 零样本克隆 | → GPT-SoVITS 精调各角色模型 | 声线稳定性大幅提升，不再依赖参考音频选择 | | ACE-Step 生成全部音效 | → Stable Audio Open 1.5 补充精细音效 | 44.1kHz 立体声，电话底噪/UI 音效品质更高 | -| 单一 TTS 引擎 | → Fish Speech S2 补充情感场景 | 醉酒、哽咽等极端情绪表达更自然 | | 结局仅语音 + 字幕 | → ACE-Step/DiffRhythm 生成结局专属 BGM | 5 个结局各有独立氛围音乐，沉浸感提升 |