--- title: Speech to Text API - AWS Transcribe & Bedrock Audio Models description: Transcribe audio to text with AWS Transcribe or AWS Bedrock audio-capable models. OpenAI-compatible STT API supporting 100+ languages, speaker diarization, and multiple output formats. keywords: speech to text API, audio transcription API, AWS Transcribe API, STT API, OpenAI Whisper alternative, audio to text, transcription service, speaker diarization --- # Speech to Text API Transcribe audio to text with AWS Transcribe or AWS Bedrock audio-capable models through an OpenAI-compatible interface. ## Why Choose Speech to Text?
- :material-translate: __Multiple Transcription Options__
Choose AWS Transcribe for 100+ languages with speaker diarization, or use Bedrock audio models for advanced capabilities. - :material-clock-fast: __Real-Time or Batch__
Stream transcriptions in real-time via SSE or process files efficiently with either service. - :material-subtitles: __Subtitle Generation__
Generate SRT and VTT subtitle files directly with precise timing for video content. - :material-account-multiple: __Advanced Features__
Speaker diarization, word-level timestamps, and automatic language detection. Feature availability varies by model choice.
## Quick Start: Available Endpoint | Endpoint | Method | What It Does | Powered By | MCP Tool | |-----------------------------|--------|------------------------------------------|-------------------------------------------|---------------------------| | `/v1/audio/transcriptions` | POST | Convert spoken audio to written text | AWS Transcribe or AWS Bedrock Audio Models | `openai_audio_transcription` | ## Feature Compatibility
| Feature | Status | Notes | |----------------------------|:---------------------------------------:|------------------------------------------------------| | **Input** | | | | Audio file upload | :material-check-circle:{ .success } | Multipart file upload | | JSON body input | :material-plus-circle:{ .extra-feature }| Base64, data URI, HTTPS URL, or S3 URI — for MCP / AI agents | | **Output Formats** | | | | `json` | :material-check-circle:{ .success } | Structured transcription | | `text` | :material-check-circle:{ .success } | Plain text output | | `verbose_json` | :material-cog:{ .model-dep } | With timestamps and details | | `diarized_json` | :material-cog:{ .model-dep } | With speaker identification | | `srt` | :material-cog:{ .model-dep } | Subtitle format with timing | | `vtt` | :material-cog:{ .model-dep } | WebVTT subtitle format | | **Language** | | | | Language specification | :material-cog:{ .model-dep } | ISO-639-1 language codes | | Auto language detection | :material-check-circle:{ .success } | Automatic identification | | **Streaming** | | | | SSE streaming | :material-check-circle:{ .success } | Event-based streaming | | **Advanced** | | | | Timestamp granularity | :material-check-circle:{ .success } | Word or segment level | | Speaker diarization | :material-check-circle:{ .success } | Automatic speaker separation | | `known_speaker_names` | :material-close-circle:{ .unsupported } | Not available | | `known_speaker_references` | :material-close-circle:{ .unsupported } | Not available | | `chunking_strategy` | :material-minus-circle:{ .partial } | Only `auto` is supported | | `temperature` | :material-cog:{ .model-dep } | Model temperature | | `prompt` | :material-cog:{ .model-dep } | Extra transcription prompt | | `logprobs` | :material-cog:{ .model-dep } | Log probabilities for token-level confidence scoring | | **Usage tracking** | | | | Input audio duration | :material-check-circle:{ .success } | Seconds (billing unit on AWS Transcribe) | | Output text tokens | :material-cog:{ .model-dep } | On models from Bedrock |
**Legend:** * :material-check-circle:{ .success } **Supported** — Fully compatible with OpenAI API * :material-cog:{ .model-dep } **Available on Select Models** — Check your model's capabilities * :material-minus-circle:{ .partial } **Partial** — Supported with limitations * :material-close-circle:{ .unsupported } **Unsupported** — Not available in this implementation
## Model Support ### ![AWS Transcribe](styles/logo_amazon_transcribe.svg){ style="height: 1.2em; vertical-align: text-bottom;" } Amazon Models | Model | Supported Languages | Notes | |-------------------|---------------------|------------------------------------------------------------------------------------------------------------| | amazon.transcribe | 100+ | Full-featured transcription with speaker diarization and subtitle generation at the cost of higher latency | !!! warning "Configuration Required" You must configure the `AWS_S3_BUCKET` or `AWS_TRANSCRIBE_S3_BUCKET` environment variable with a bucket in the main AWS region to use this model. This bucket is used for temporary storage during transcription processing. ### ![Mistral](styles/logo_mistralai.svg){ style="height: 1.2em; vertical-align: text-bottom;" } Mistral Models | Model | Supported Languages | Notes | |---------------------------------|---------------------|----------------------------------------------------| | mistral.voxtral-mini-3b-2507 | 100+ | Compact model for fast transcription | | mistral.voxtral-small-24b-2507 | 100+ | Larger model for enhanced accuracy | !!! warning "Mistral Voxtral Limitations" Mistral Voxtral models have the following restrictions when running on AWS Bedrock: - **File size limit**: ~2MB maximum input file size - **Audio channels**: Mono channel audio only (single channel) ## Advanced Features ### ![AWS Transcribe](styles/logo_amazon_transcribe.svg){ style="height: 1.2em; vertical-align: text-bottom;" } Amazon Transcribe Features **Model & Features:** - Use `amazon.transcribe` with the same interface as OpenAI's Whisper API - **Or use OpenAI model name directly**: `whisper-1` works out of the box (maps to `amazon.transcribe`) - Auto-detect language or specify it for faster processing - Word-level or segment-level timestamps with `verbose_json` - **Speaker Diarization** :material-account-multiple:{ .highlight }: Automatically identify and label different speakers with `diarized_json` - **Native Subtitles** :material-file-video:{ .highlight }: SRT/VTT files generated directly by AWS Transcribe with precise timing !!! tip "OpenAI Model Compatibility" stdapi.ai includes a built-in model alias that maps the OpenAI model name to AWS Transcribe: - `whisper-1` → `amazon.transcribe` This alias enables seamless compatibility with OpenAI-based tools and applications without any configuration changes. You can also [customize or override this alias](operations_configuration.md#model-aliases) to suit your needs. **Note:** The `prompt`, `temperature`, `chunking_strategy`, `known_speaker_names`, and `known_speaker_references` parameters are not supported to ensure consistent transcription accuracy. AWS Transcribe provides automatic speaker diarization without requiring known speaker references. !!! tip "Performance Tips: Optimize Speed & Cost" - **Specify the language** if you know it—skips auto-detection for faster processing and lower AWS costs ## Try It Now **Transcribe audio to JSON:** ```bash curl -X POST "$BASE/v1/audio/transcriptions" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -F file=@meeting-recording.mp3 \ -F model=amazon.transcribe \ -F response_format=json ``` **Transcribe via JSON body (MCP and AI agents):** When using MCP tools or HTTP clients that cannot construct multipart requests, pass the audio as a data URI or URL: ```bash # Data URI (inline base64) curl -X POST "$BASE/v1/audio/transcriptions" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "file": "data:audio/mp3;base64,", "model": "amazon.transcribe", "response_format": "json" }' ``` ```bash # HTTPS URL (server fetches the audio) curl -X POST "$BASE/v1/audio/transcriptions" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "file": "https://example.com/audio.mp3", "model": "amazon.transcribe" }' ``` **Generate subtitles:** ```bash curl -X POST "$BASE/v1/audio/transcriptions" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -F file=@video-audio.mp3 \ -F model=amazon.transcribe \ -F response_format=srt \ -F language=en ``` **Transcribe with speaker diarization:** ```bash curl -X POST "$BASE/v1/audio/transcriptions" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -F file=@meeting-recording.mp3 \ -F model=amazon.transcribe \ -F response_format=diarized_json ``` --- **Ready to transcribe audio?** Explore available transcription models in the [Models API](api_openai_models.md).