react-native-executorch/lib/module/hooks/natural_language_processing/useSpeechToText.js

An easy way to run AI models in React Native with ExecuTorch

"use strict";

import { useEffect, useCallback, useState } from 'react';
import { SpeechToTextModule } from '../../modules/natural_language_processing/SpeechToTextModule';
import { RnExecutorchErrorCode } from '../../errors/ErrorCodes';
import { RnExecutorchError, parseUnknownError } from '../../errors/errorUtils';

/**
 * React hook for managing a Speech to Text (STT) instance.
 * @category Hooks
 * @param speechToTextProps - Configuration object containing `model` source and optional `preventLoad` flag.
 * @returns Ready to use Speech to Text model.
 */
export const useSpeechToText = ({
  model,
  preventLoad = false
}) => {
  const [error, setError] = useState(null);
  const [isReady, setIsReady] = useState(false);
  const [isGenerating, setIsGenerating] = useState(false);
  const [downloadProgress, setDownloadProgress] = useState(0);
  const [moduleInstance, setModuleInstance] = useState(null);
  useEffect(() => {
    if (preventLoad) return;
    let active = true;
    setDownloadProgress(0);
    setError(null);
    setIsReady(false);
    SpeechToTextModule.fromModelName({
      modelName: model.modelName,
      isMultilingual: model.isMultilingual,
      modelSource: model.modelSource,
      tokenizerSource: model.tokenizerSource
    }, p => {
      if (active) setDownloadProgress(p);
    }).then(mod => {
      if (!active) {
        mod.delete();
        return;
      }
      setModuleInstance(prev => {
        prev?.delete();
        return mod;
      });
      setIsReady(true);
    }).catch(err => {
      if (active) setError(parseUnknownError(err));
    });
    return () => {
      active = false;
      setModuleInstance(prev => {
        prev?.delete();
        return null;
      });
    };
  }, [model.modelName, model.isMultilingual, model.modelSource, model.tokenizerSource, preventLoad]);
  const transcribe = useCallback(async (waveform, options = {}) => {
    if (!isReady || !moduleInstance) {
      throw new RnExecutorchError(RnExecutorchErrorCode.ModuleNotLoaded, 'The model is currently not loaded. Please load the model before calling this function.');
    }
    if (isGenerating) {
      throw new RnExecutorchError(RnExecutorchErrorCode.ModelGenerating, 'The model is currently generating. Please wait until previous model run is complete.');
    }
    setIsGenerating(true);
    try {
      return await moduleInstance.transcribe(waveform, options);
    } finally {
      setIsGenerating(false);
    }
  }, [isReady, isGenerating, moduleInstance]);
  const stream = useCallback(async function* (options = {}) {
    if (!isReady || !moduleInstance) {
      throw new RnExecutorchError(RnExecutorchErrorCode.ModuleNotLoaded, 'The model is currently not loaded. Please load the model before calling this function.');
    }
    if (isGenerating) {
      throw new RnExecutorchError(RnExecutorchErrorCode.ModelGenerating, 'The model is currently generating. Please wait until previous model run is complete.');
    }
    setIsGenerating(true);
    try {
      const generator = moduleInstance.stream(options);
      for await (const result of generator) {
        yield result;
      }
    } finally {
      setIsGenerating(false);
    }
  }, [isReady, isGenerating, moduleInstance]);
  const streamInsert = useCallback(waveform => {
    if (!isReady || !moduleInstance) return;
    moduleInstance.streamInsert(waveform);
  }, [isReady, moduleInstance]);
  const streamStop = useCallback(() => {
    if (!isReady || !moduleInstance) return;
    moduleInstance.streamStop();
  }, [isReady, moduleInstance]);
  const encode = useCallback(waveform => {
    if (!moduleInstance) throw new RnExecutorchError(RnExecutorchErrorCode.ModuleNotLoaded, 'The model is currently not loaded. Please load the model before calling this function.');
    return moduleInstance.encode(waveform);
  }, [moduleInstance]);
  const decode = useCallback((tokens, encoderOutput) => {
    if (!moduleInstance) throw new RnExecutorchError(RnExecutorchErrorCode.ModuleNotLoaded, 'The model is currently not loaded. Please load the model before calling this function.');
    return moduleInstance.decode(tokens, encoderOutput);
  }, [moduleInstance]);
  return {
    error,
    isReady,
    isGenerating,
    downloadProgress,
    transcribe,
    stream,
    streamInsert,
    streamStop,
    encode,
    decode
  };
};
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