Install additional dependencies for batch processing

Windows PowerShell

Clone a character voice from sample recordings

with open(“samples/knight_commander_01.mp3”, “rb”) as f1,
open(“samples/knight_commander_02.mp3”, “rb”) as f2: voice = client.voices.add( name=“Knight Commander Aldric”, description=“Deep, authoritative male voice. Mid-40s. Battle-worn leader.”, files=[f1, f2], labels={“character”: “aldric”, “game”: “echoes_of_avalon”} )

client = ElevenLabs(api_key=os.getenv(“ELEVENLABS_API_KEY”))

TARGET_LANGUAGES = [ “en”, “ja”, “ko”, “zh”, “de”, “fr”, “es”, “pt”, “it”, “pl”, “ru”, “ar” ]

def generate_dialogue(csv_path: str, output_dir: str): df = pd.read_csv(csv_path) # columns: line_id, character, voice_id, text, lang

for _, row in tqdm(df.iterrows(), total=len(df)):
    for lang in TARGET_LANGUAGES:
        out_path = os.path.join(
            output_dir, lang, row["character"], f"{row['line_id']}.mp3"
        )
        os.makedirs(os.path.dirname(out_path), exist_ok=True)
        
        # Skip if already generated
        if os.path.exists(out_path):
            continue
        
        audio_generator = client.text_to_speech.convert(
            voice_id=row["voice_id"],
            text=row[f"text_{lang}"],  # Pre-translated column
            model_id="eleven_turbo_v2_5",
            language_code=lang,
            voice_settings={
                "stability": 0.55,
                "similarity_boost": 0.80,
                "style": 0.35,
                "use_speaker_boost": True
            }
        )
        
        audio_bytes = b"".join(audio_generator)
        with open(out_path, "wb") as f:
            f.write(audio_bytes)

# Flag if duration differs by more than 40% from English baseline
if ratio < (1 - tolerance) or ratio > (1 + tolerance):
    return {"status": "REVIEW", "reason": "duration_mismatch", "ratio": round(ratio, 2)}

# Check for excessive silence (more than 2s consecutive)
silence_threshold = -40  # dBFS
silent_chunks = [chunk for chunk in audio[::100] if chunk.dBFS < silence_threshold]
silence_ratio = len(silent_chunks) / (len(audio) / 100)

if silence_ratio > 0.3:
    return {"status": "REVIEW", "reason": "excessive_silence", "silence": round(silence_ratio, 2)}

return {"status": "PASS"}</code></pre>

Step 5: Export and Integration with Game Engine

The final audio files follow a naming convention that maps directly to the game's dialogue system: output/voiced/ ├── en/ │ ├── aldric/ │ │ ├── ACT1_SCENE3_001.mp3 │ │ ├── ACT1_SCENE3_002.mp3 │ └── lyra/ │ ├── ACT1_SCENE1_001.mp3 ├── ja/ │ ├── aldric/ │ │ ├── ACT1_SCENE3_001.mp3 ...

The game engine loads dialogue by constructing the path from the player's language setting, character ID, and line ID—no code changes required compared to the traditional voice acting pipeline.

Results Summary

• 70% cost reduction: $126,000 vs. $420,000 traditional quote- 85% faster production: 6 weeks vs. 8 months- Iteration capability: Script changes regenerated in minutes, not weeks- Consistency: Character voices remain identical across all 12 languages- Late-stage flexibility: Added 1,200 lines of new dialogue in final QA without schedule impact

Pro Tips for Power Users

• Use eleven_turbo_v2_5 for batch work: It is faster and cheaper than the standard multilingual model while maintaining quality for game dialogue.- Tune stability per character archetype: Lower stability (0.3–0.5) for emotional or erratic characters; higher (0.6–0.8) for calm narrators and authority figures.- Batch by character, not by scene: Processing all lines for one voice_id sequentially reduces API overhead and keeps voice consistency higher.- Cache voice settings per character in a JSON config rather than hardcoding—this lets voice directors iterate without touching code.- Use the Projects feature in ElevenLabs for long-form cutscene monologues where paragraph-level context improves pacing and intonation.

Troubleshooting Common Issues

Do voice actors need to consent to having their voice cloned for multilingual use?

Yes. ElevenLabs requires explicit consent from the original voice actor before creating a clone. Pixel Forge included AI voice synthesis rights in their voice acting contracts, with actors receiving a flat licensing fee covering all 12 language outputs. This is both an ethical requirement and an ElevenLabs platform policy—uploading voice samples without consent can result in account termination.

How does the audio quality compare to native-speaking voice actors?

For game dialogue—short to medium lines with clear emotional direction—the quality is production-ready for most languages. Pixel Forge’s internal testing showed 92% of generated lines passed QA without manual intervention. The remaining 8% required parameter tuning or text adjustments. Languages with complex prosody (Japanese, Arabic) needed slightly more QA passes. For AAA cinematic cutscenes with nuanced emotional range, a hybrid approach combining AI generation with selective native actor recording may be more appropriate.

What ElevenLabs plan is needed for a project of this scale?

A project with 85,000 words across 12 languages generates roughly 1.02 million characters of text-to-speech. The Scale plan (starting at $99/month with 2 million characters included) covers this comfortably within one billing cycle. For studios needing higher concurrency, custom voice limits, or SLA guarantees, the Enterprise plan provides dedicated capacity and priority support. Character usage can be monitored via the API with client.user.get() to track remaining quota.