Audioshake vs Phind

Side-by-side comparison · Updated September 2026

 AudioshakeAudioshakePhindPhind
DescriptionAudioShake 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.Phind is an AI-powered search engine specifically designed for developers and technical professionals, offering quick and accurate answers to complex queries. It integrates into development environments like Visual Studio Code, providing context-aware search results, often with coding snippets and documentation. Phind supports up to 32K token context windows, ensuring comprehensive responses. Operating on the advanced Phind-70B model, it also boasts superior speed and accuracy for technical queries, outperforming traditional search engines and even GPT-4. Available across several platforms, it offers flexible pricing with free and pro plans.
CategoryAudio EditingSearch Engine
RatingNo reviewsNo reviews
PricingUsage-BasedFreemium
Starting PriceN/AFree
Plans
  • Free TierFree
  • Phind Plus$15/mo
  • Phind Pro - Monthly$30/mo
  • Phind Pro - Annual$300/yr
  • Enterprise PlanContact for pricing
Use Cases
  • Audio Engineers
  • Film Editors
  • Content Creators
  • Musicians
  • Software Developers
  • Technical Support Engineers
  • Project Managers
  • Software Architects
Tags
audio source separationmusic stem separationdialogue separationlyric alignmentaudio API
AIsearch enginedeveloperstechnical professionalsVisual Studio Code integration
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
AI-powered search for technical queries
Integration with Visual Studio Code
Fast, context-aware responses
Access to extensive technical documentation
Codebase integration for context-specific answers
Seamless conversation follow-up
Multi-platform availability
Superior speed and accuracy
Large context window of up to 32K tokens
Advanced model outperforming GPT-4
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