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# whisper.cpp/examples/server
Simple http server. WAV Files are passed to the inference model via http requests.
https://github.com/ggerganov/whisper.cpp/assets/1991296/e983ee53-8741-4eb5-9048-afe5e4594b8f
## Usage
```
./build/bin/whisper-server -h
usage: ./build/bin/whisper-server [options]
options:
-h, --help [default] show this help message and exit
-t N, --threads N [4 ] number of threads to use during computation
-p N, --processors N [1 ] number of processors to use during computation
-ot N, --offset-t N [0 ] time offset in milliseconds
-on N, --offset-n N [0 ] segment index offset
-d N, --duration N [0 ] duration of audio to process in milliseconds
-mc N, --max-context N [-1 ] maximum number of text context tokens to store
-ml N, --max-len N [0 ] maximum segment length in characters
-sow, --split-on-word [false ] split on word rather than on token
-bo N, --best-of N [2 ] number of best candidates to keep
-bs N, --beam-size N [-1 ] beam size for beam search
-wt N, --word-thold N [0.01 ] word timestamp probability threshold
-et N, --entropy-thold N [2.40 ] entropy threshold for decoder fail
-lpt N, --logprob-thold N [-1.00 ] log probability threshold for decoder fail
-debug, --debug-mode [false ] enable debug mode (eg. dump log_mel)
-tr, --translate [false ] translate from source language to english
-di, --diarize [false ] stereo audio diarization
-tdrz, --tinydiarize [false ] enable tinydiarize (requires a tdrz model)
-nf, --no-fallback [false ] do not use temperature fallback while decoding
-ps, --print-special [false ] print special tokens
-pc, --print-colors [false ] print colors
-pr, --print-realtime [false ] print output in realtime
-pp, --print-progress [false ] print progress
-nt, --no-timestamps [false ] do not print timestamps
-l LANG, --language LANG [en ] spoken language ('auto' for auto-detect)
-dl, --detect-language [false ] exit after automatically detecting language
--prompt PROMPT [ ] initial prompt
-m FNAME, --model FNAME [models/ggml-base.en.bin] model path
-oved D, --ov-e-device DNAME [CPU ] the OpenVINO device used for encode inference
--host HOST, [127.0.0.1] Hostname/ip-adress for the server
--port PORT, [8080 ] Port number for the server
--convert, [false ] Convert audio to WAV, requires ffmpeg on the server
```
> [!WARNING]
> **Do not run the server example with administrative privileges and ensure it's operated in a sandbox environment, especially since it involves risky operations like accepting user file uploads and using ffmpeg for format conversions. Always validate and sanitize inputs to guard against potential security threats.**
## request examples
**/inference**
```
curl 127.0.0.1:8080/inference \
-H "Content-Type: multipart/form-data" \
-F file="@<file-path>" \
-F temperature="0.0" \
-F temperature_inc="0.2" \
-F response_format="json"
```
**/load**
```
curl 127.0.0.1:8080/load \
-H "Content-Type: multipart/form-data" \
-F model="<path-to-model-file>"
```
## Load testing with k6
> **Note:** Install [k6](https://k6.io/docs/get-started/installation/) before running the benchmark script.
You can benchmark the Whisper server using the provided bench.js script with [k6](https://k6.io/). This script sends concurrent multipart requests to the /inference endpoint and is fully configurable via environment variables.
**Example usage:**
```
k6 run bench.js \
--env FILE_PATH=/absolute/path/to/samples/jfk.wav \
--env BASE_URL=http://127.0.0.1:8080 \
--env ENDPOINT=/inference \
--env CONCURRENCY=4 \
--env TEMPERATURE=0.0 \
--env TEMPERATURE_INC=0.2 \
--env RESPONSE_FORMAT=json
```
**Environment variables:**
- `FILE_PATH`: Path to the audio file to send (must be absolute or relative to the k6 working directory)
- `BASE_URL`: Server base URL (default: `http://127.0.0.1:8080`)
- `ENDPOINT`: API endpoint (default: `/inference`)
- `CONCURRENCY`: Number of concurrent requests (default: 4)
- `TEMPERATURE`: Decoding temperature (default: 0.0)
- `TEMPERATURE_INC`: Temperature increment (default: 0.2)
- `RESPONSE_FORMAT`: Response format (default: `json`)
**Note:**
- The server must be running and accessible at the specified `BASE_URL` and `ENDPOINT`.
- The script is located in the same directory as this README: `bench.js`.
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