Local AI music generation removes the meter from creativity. Tools like ACE-Step 1.5 can run entirely on a Mac, turning text prompts into full songs in minutes with no cloud credits and no upload queues. That freedom breeds a new problem: volume. When generation is cheap and instant, you stop asking whether you can make a song and start wondering which of the fifty versions on your drive is worth keeping.

I learned this the hard way. On an average day, I need about four takes from ACE-Step 1.5 to find one keeper. But averages lie. In one recent session, I generated thirty-two takes chasing a single arrangement. Listening to thirty-two two-minute songs is over an hour of critical listening. By the seventh track, my ears were forgiving smeared vowels. By the fifteenth, I was nodding along to melodies while missing dropped words entirely. Listener fatigue is not laziness; it is a real collapse in perceptual accuracy. I needed a filter that could run before my ears did.

So I built a local QA pipeline around mlx-whisper, the Apple Silicon-optimized port of OpenAI’s speech-recognition model. The idea was simple: if I could transcribe every take automatically and compare it to the original lyrics, I would have an objective lyric-match rate. That number could rank the thirty-two takes down to a manageable handful.

How the Pipeline Works

The workflow has four stages, and I treat each one as non-negotiable.

Generate. I create the raw audio with ACE-Step 1.5. I do not judge anything at this stage. The goal is volume.

Transcribe. Every WAV file feeds into mlx-whisper on my Mac. Because MLX is built for Apple’s Metal and the neural engine, this runs entirely locally. There are no API costs, no network latency, and no privacy concerns about shipping raw audio to a remote server. A thirty-two-file batch transcribes while I make coffee.

Score. I compare the Whisper transcript against the original lyric prompt. The match rate measures fidelity: did the singer hit every word, or did it skip lines, slur phrases, or hallucinate syllables? I calculate a percentage overlap. This is not an aesthetic judgment; it is a strict accounting of textual accuracy.

Filter. I sort the takes by that match rate. The top quintile becomes my audition pool. Everything else goes to a secondary folder. I have not deleted the low scorers yet, but I do not waste prime listening time on them.

Keep the Jury Independent

I follow one hard rule: the generation model never scores its own homework. ACE-Step 1.5 does not evaluate ACE-Step 1.5 output. I use an entirely separate process for transcription because a model that checks itself will always be too kind. It shares the same blind spots. If the generator tends to drop plural markers or softens hard consonants, a self-evaluation loop will learn to ignore those same quirks. An independent speech-recognition model has no loyalty to the music. It simply reports what it hears, however harsh that report might be.

What the Numbers Revealed

On the batch of thirty-two takes, the pipeline narrowed the field to eight candidates that were worth serious attention. Those eight averaged an 83.9% lyric-match rate. After I listened to them properly and scored them against my own quality rubric, they averaged 94.1 out of 100. That spread tells the whole story. The machine gate caught the obvious structural failures—lyric drops, timing collapses, vocal artifacts—so that my human scoring could operate on a pre-cleaned set. The automation did not replace my judgment. It conserved it.

When the Machine Misfires

A low score is not always a bad song. I caught this early when a track I loved scored far below the cutoff. The lyrics were a simple alphabet chant: single letters sung as isolated sounds. Whisper is trained on natural sentence structure. Feed it "A B C D" and it often hallucinates words, inserts articles, or collapses the letters into garbled phonemes. The transcription failed, but the vocal performance was actually crisp.

That case taught me the practical limit. The match rate is a pre-filter, not a final verdict. Any take that scores low still gets a ten-second human audition before I trash it. The number points you toward probability, not certainty. If you treat the score as a gavel rather than a compass, you will throw away good music.

A Technical Speed Bump

Apple Silicon上でMLXを実行している場合は、大量のバッチを処理する前にPythonのアーキテクチャを確認してください。よくある設定ミスは、Rosettaエミュレーション経由でPythonバイナリを実行してしまうことです。スクリプト自体は実行されますが、ローカルでの文字起こしを現実的な速度にするためのハードウェアアクセラレーションが失われてしまいます。

ターミナルで以下を実行してください：

python3 -c "import platform; print(platform.machine())"

arm64 と表示される必要があります。もし x86_64 と表示されたら、環境がエミュレートされています。ネイティブなPythonビルドまたはネイティブなconda環境に切り替え、その後 mlx-whisper を再インストールしてください。長いバッチ処理において、エミュレート実行とネイティブ実行の差は、「昼食前に終わるか、夕食前に終わるか」ほどの違いになります。

真の価値

生成AIによるオーディオ制作は粘り強さが報われるものですが、人間の集中力には限りがあります。自分が徹底していることを証明するためだけに、32回もの平凡なテイクを聴き続けることに、何の栄光もありません。生成器と自分の耳の間に mlx-whisper を挟むことで、集中してクリエイティブに取り組める時間を何時間も取り戻すことができました。どの曲を残し、どの曲を捨てるかを決めるのは、依然として私自身です。マシンは単に、私がその判断を最高の候補に対して行えるようにしてくれるだけなのです。

このパイプラインに関する詳細な解説はこちらで読むことができます。同様のローカルQAツールを構築しているなら、GyaanSetu community は情報交換に最適な場所です。