Method of training speech recognition model, electronic device and storage medium

An electronic device, comprising:

• a memory and one or more processors;
• wherein the memory is configured to store a computer program executable by the one or more processors;
• and
• wherein the one or more processors are configured to execute the computer program in the memory to implement acts comprising:
• for each of a plurality of training samples,
• inputting speech data of a training sample into a teacher model and a to-be-trained speech recognition model separately,
• obtaining an embedding outputted by the teacher model and encoded data outputted by the to-be-trained speech recognition model, wherein the embedding comprises a floating-point vector holding D floating-point numbers,
• obtaining quantized codebook data by performing a multi-codebook quantization on the embedding, wherein the quantized codebook data comprises N integers corresponding to the speech data and each integer indicates a codebook index, wherein N is a positive integer,
• calculating a loss based on the encoded data, the quantized codebook data, and text data in the training sample, and
• obtaining a trained speech recognition model by stopping training the to-be-trained speech recognition model in response to determining at least one of followings:
• the loss being less than or equal to a preset loss threshold, or trained times being greater than preset trained times.