Audioshake vs Metaphysic
Side-by-side comparison · Updated November 2026
| Description | AudioShake separates an existing recording into sources such as vocals, instruments, dialogue, music and effects. It also offers speech cleanup, speaker separation, lyric transcription and word-level lyric alignment. Use those outputs in editing, dubbing, remixing or audio analysis, and listen for leakage and changed sounds before delivery. Its lyric transcription is not a general-purpose speech-to-text service; separated speech can instead feed a dedicated transcription workflow. The developer API includes 10 credits on signup. Usage is billed per source-audio minute for each target model, rounding the duration up to the next minute. A 2 minute 10 second recording processed by vocals and instrumental models at one credit per minute each costs six credits. Instrument stems and lyric transcription/alignment cost one credit per minute; dialogue, effects and speech denoise cost 1.5, dereverb two, multi-voice and music removal ten, and music detection 0.5. Credit rates are not dollar prices. Dereverb also reduces noise, so avoid automatically paying for both cleanup models when one meets the task. Choose AudioShake Live for its professional web workflow, Indie for its artist-oriented route, the API for asynchronous jobs and batches, or a separately licensed Local Inference SDK for on-device or self-hosted integration. Check the target model's input limits: multi-voice accepts at most 1.5 hours, while lyric transcription and alignment accept at most 45 minutes. Pilot the required source material and integration before committing to a production pipeline. AudioShake's August 2026 terms identify Audioshake, Inc. Customers retain content ownership and must have the rights to upload and process it. The agreement permits service improvement and development uses of content, while promising not to sell it or use it to train models designed to generate new original musical compositions or sound recordings. That is a narrower promise than a ban on all model training. Confirm the applicable Order Form for third-party or service-bureau integrations and agree retention requirements for confidential audio. | Text-to-image and text-to-video models like Stable Diffusion and Sora depend on image datasets with accurate captions, which are often flawed or incomplete. This flaw leads to potential issues in generative AI outputs. The main challenge is developing datasets with captions that are both comprehensive and precise, an issue that current large language models might not solve effectively. |
| Category | Audio Editing | Data Management |
| Rating | No reviews | No reviews |
| Pricing | Usage-Based | Pricing unavailable |
| Starting Price | N/A | N/A |
| Use Cases |
|
|
| Tags | audio source separationmusic stem separationdialogue separationlyric alignmentaudio API | Text-To-ImageText-To-VideoDatasetStable DiffusionSora |
| Features | ||
| Music and instrument stem separation | ||
| Dialogue, music and effects separation | ||
| Speech denoise and combined dereverb/denoise | ||
| Multi-speaker separation | ||
| Lyric transcription and word-level alignment | ||
| Asynchronous API jobs, webhooks and batches | ||
| Separate Live, Indie and Local Inference SDK access | ||
| Dependency on accurate captioning | ||
| Challenges with flawed datasets | ||
| Issues in generative AI outputs | ||
| Limitations of large language models | ||
| Need for comprehensive datasets | ||
| Impact on user experience | ||
| Ongoing efforts for improvement | ||
| Importance in text-to-image and text-to-video models | ||
| Collaborative efforts required | ||
| Potential future developments | ||
| View Audioshake | View Metaphysic | |
Modify This Comparison
Also Compare
Explore more head-to-head comparisons with Audioshake and Metaphysic.