Google Cloud Speech-to-Text vs Whisper

Google Cloud Speech-to-Text converts audio into text and provides APIs for adding speech recognition to applications. It offers synchronous, asynchronous, and streaming recognition for post-processing, periodic, or real-time results, and lists support for 85+ languages and variants. Model adaptation lets users provide hints for domain-specific terms, rare words, and phrases. Speaker diarization can predict which speaker produced each utterance; the service also supports multichannel recognition and says it can handle noisy audio without additional noise cancellation. Specialized models are available for voice control, phone calls, and video transcription. API v2 includes data residency, audit logging, and customer-managed encryption keys. Speech-to-Text On-Prem runs in private data centers and is offered through a sales contact. Developer access includes REST and RPC APIs, client libraries, and command-line quickstarts. Listed pricing includes Speech-to-Text V2 API standard recognition models at $0.02 a month, billed at $0.016 / 1 minute for 0–500,000 minutes, with lower rates at higher monthly volumes; V1 standard plans include 60 free minutes per month. Costs depend on duration, channel count, model, batch method, and API version, with each channel billed separately.

Whisper is an open-source speech recognition model for transcription, speech translation, language identification, and voice activity detection. Its documented command-line and Python interfaces process audio files, and it can translate speech from multiple languages into English. The repository lists six model sizes—tiny, base, small, medium, large, and turbo—with different speed and memory tradeoffs; stated VRAM needs range from about 1 GB to 10 GB, depending on size. Installation is documented through pip and requires the ffmpeg command-line tool. Rust may be needed if a prebuilt tiktoken wheel is unavailable. The code and model weights are released under the MIT License, with local installation using Python and ffmpeg. Whisper is listed for Linux, macOS, Windows, and self-hosted deployment. OpenAI says it was trained on 680,000 hours of multilingual and multitask supervised data collected from the web. Performance varies widely by language, and it does not outperform models specialized for LibriSpeech. Turbo is not trained for translation; multilingual models are recommended for translating non-English speech into English.