Summary
Speech-to-text API for Latin American Spanish calls, with batch transcription and optional word-level speaker diarization.
About
Orchard provides a specialized transcription API for Latin American Spanish calls, combining compatibility with OpenAI-style workflows and published benchmark materials.
Orchard is a speech-to-text API designed for real-world calls in Latin American Spanish. Batch transcription is priced at $0.025 per hour, and transcription with word-level speaker diarization is priced at $0.036 per hour. The API is OpenAI-compatible, allowing integration through a one-line change for compatible workflows. The team benchmarked seven engines on real calls and published the audio, reference transcripts, and code.
Key Features
Speech-to-text transcription for Latin American Spanish calls
Batch transcription priced at $0.025 per hour
Optional word-level speaker diarization priced at $0.036 per hour
OpenAI-compatible API intended to simplify integration
Published benchmark audio, reference transcripts, and code comparing seven engines
Real-World Use Cases
Call transcription
Convert recorded Latin American Spanish customer or business calls into text for review, search, and downstream analysis.
Speaker-attributed call records
Use word-level speaker diarization to associate transcribed words with speakers in recorded conversations.
Speech recognition evaluation
Use the published audio, references, and code to assess transcription performance against other engines on real calls.
Business Opportunities
Automated contact-center transcription
Problem: Organizations handling Latin American Spanish calls may need searchable records and may find general-purpose transcription insufficiently suited to their call audio.
Solution: Integrate Orchard to transcribe recorded calls, with optional word-level speaker diarization for speaker-attributed records.
Estimated ROI: Potentially positive where transcription reduces manual review effort or enables analysis of call records; actual returns depend on call volume, accuracy, and integration costs.
Execution: Connect the API to call-recording workflows, evaluate output on representative recordings, and route transcripts into existing search, quality-assurance, or analytics systems.
Spanish-language conversation analytics
Problem: Businesses may lack structured text and speaker-level data for analyzing large volumes of Latin American Spanish conversations.
Solution: Use batch transcription and diarization as inputs to internal reporting, quality monitoring, or analytics applications.
Estimated ROI: Potentially moderate to high for organizations with substantial call volumes and clear operational decisions tied to conversation insights; no return is guaranteed.
Execution: Build a processing pipeline that submits recordings, stores transcripts and speaker labels, and applies organization-specific analysis to the resulting data.