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| import type { TaskDataCustom } from "../Types"; | |
| const taskData: TaskDataCustom = { | |
| datasets: [ | |
| { | |
| description: "A benchmark of 10 different audio tasks.", | |
| id: "superb", | |
| }, | |
| ], | |
| demo: { | |
| inputs: [ | |
| { | |
| filename: "audio.wav", | |
| type: "audio", | |
| }, | |
| ], | |
| outputs: [ | |
| { | |
| data: [ | |
| { | |
| label: "Up", | |
| score: 0.2, | |
| }, | |
| { | |
| label: "Down", | |
| score: 0.8, | |
| }, | |
| ], | |
| type: "chart", | |
| }, | |
| ], | |
| }, | |
| metrics: [ | |
| { | |
| description: "", | |
| id: "accuracy", | |
| }, | |
| { | |
| description: "", | |
| id: "recall", | |
| }, | |
| { | |
| description: "", | |
| id: "precision", | |
| }, | |
| { | |
| description: "", | |
| id: "f1", | |
| }, | |
| ], | |
| models: [ | |
| { | |
| description: "An easy-to-use model for Command Recognition.", | |
| id: "speechbrain/google_speech_command_xvector", | |
| }, | |
| { | |
| description: "An Emotion Recognition model.", | |
| id: "ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition", | |
| }, | |
| { | |
| description: "A language identification model.", | |
| id: "facebook/mms-lid-126", | |
| }, | |
| ], | |
| spaces: [ | |
| { | |
| description: "An application that can predict the language spoken in a given audio.", | |
| id: "akhaliq/Speechbrain-audio-classification", | |
| }, | |
| ], | |
| summary: | |
| "Audio classification is the task of assigning a label or class to a given audio. It can be used for recognizing which command a user is giving or the emotion of a statement, as well as identifying a speaker.", | |
| widgetModels: ["facebook/mms-lid-126"], | |
| youtubeId: "KWwzcmG98Ds", | |
| }; | |
| export default taskData; | |