# PyAnnote REST API
## Installation
$ pip install pyannote.server
## Running the server
$ python -m pyannote.server.run
## Using the API
### Parsers
* `/parser/` returns list of supported file formats
$ curl -X GET http://localhost:5000/parser/
["mdtm", "uem"]
* `/parser/<format>` parses `POST`ed file and returns its content in PyAnnote JSON format.
### Evaluation metrics
* `/metric/` returns list of available evaluation metrics
$ curl -X GET http://localhost:5000/metric/
["detection", "diarization", "identification"]
* `/metric/<name>` compares `POST`ed reference and hypothesis annotations in JSON format and returns the corresponding evaluation metric.
**Input format (JSON)**
{
"reference": [
...
],
"hypothesis": [
...
]
}
**Output format (JSON)**
{
METRIC: {
METRIC: value,
COMPONENT_1: value_1,
COMPONENT_2: value_2,
... # components are values from
... # which the final value is computed
},
... # one call to /parser/<metric> may
... # return more than one sub-metrics
}
### Error analysis
* `/error/diff` compares `POST`ed reference and hypothesis and returns their differences.
**Input format (JSON)**
# same format as for metric/<name>
{
"reference": [
...
],
"hypothesis": [
...
]
}
* `/error/regression` compares `POST`ed reference with two hypotheses and returns regressions and/or improvements brought by the second one (`after`) over the first one (`before`).
**Input format (JSON)**
{
"reference": [
...
],
"before": [
...
],
"after": [
...
]
}
## Installation
$ pip install pyannote.server
## Running the server
$ python -m pyannote.server.run
## Using the API
### Parsers
* `/parser/` returns list of supported file formats
$ curl -X GET http://localhost:5000/parser/
["mdtm", "uem"]
* `/parser/<format>` parses `POST`ed file and returns its content in PyAnnote JSON format.
### Evaluation metrics
* `/metric/` returns list of available evaluation metrics
$ curl -X GET http://localhost:5000/metric/
["detection", "diarization", "identification"]
* `/metric/<name>` compares `POST`ed reference and hypothesis annotations in JSON format and returns the corresponding evaluation metric.
**Input format (JSON)**
{
"reference": [
...
],
"hypothesis": [
...
]
}
**Output format (JSON)**
{
METRIC: {
METRIC: value,
COMPONENT_1: value_1,
COMPONENT_2: value_2,
... # components are values from
... # which the final value is computed
},
... # one call to /parser/<metric> may
... # return more than one sub-metrics
}
### Error analysis
* `/error/diff` compares `POST`ed reference and hypothesis and returns their differences.
**Input format (JSON)**
# same format as for metric/<name>
{
"reference": [
...
],
"hypothesis": [
...
]
}
* `/error/regression` compares `POST`ed reference with two hypotheses and returns regressions and/or improvements brought by the second one (`after`) over the first one (`before`).
**Input format (JSON)**
{
"reference": [
...
],
"before": [
...
],
"after": [
...
]
}
Metadata
Release files for pyannote.server 0.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pyannote.server-0.7.tar.gz | 10.1 kB | Details |
Release files / pyannote.server-0.7.tar.gz
| Download URL | pyannote.server-0.7.tar.gz |
|---|---|
| Size | 10.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
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