Support RKNN for Zipformer CTC models. (#1948)
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@@ -91,30 +91,23 @@ static void DecodeOne(const float *log_probs, int32_t num_rows,
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processed_frames += num_rows;
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}
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void OnlineCtcFstDecoder::Decode(Ort::Value log_probs,
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void OnlineCtcFstDecoder::Decode(const float *log_probs, int32_t batch_size,
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int32_t num_frames, int32_t vocab_size,
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std::vector<OnlineCtcDecoderResult> *results,
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OnlineStream **ss, int32_t n) {
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std::vector<int64_t> log_probs_shape =
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log_probs.GetTensorTypeAndShapeInfo().GetShape();
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if (log_probs_shape[0] != results->size()) {
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if (batch_size != results->size()) {
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SHERPA_ONNX_LOGE("Size mismatch! log_probs.size(0) %d, results.size(0): %d",
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static_cast<int32_t>(log_probs_shape[0]),
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static_cast<int32_t>(results->size()));
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batch_size, static_cast<int32_t>(results->size()));
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exit(-1);
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}
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if (log_probs_shape[0] != n) {
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SHERPA_ONNX_LOGE("Size mismatch! log_probs.size(0) %d, n: %d",
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static_cast<int32_t>(log_probs_shape[0]), n);
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if (batch_size != n) {
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SHERPA_ONNX_LOGE("Size mismatch! log_probs.size(0) %d, n: %d", batch_size,
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n);
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exit(-1);
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}
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int32_t batch_size = static_cast<int32_t>(log_probs_shape[0]);
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int32_t num_frames = static_cast<int32_t>(log_probs_shape[1]);
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int32_t vocab_size = static_cast<int32_t>(log_probs_shape[2]);
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const float *p = log_probs.GetTensorData<float>();
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const float *p = log_probs;
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for (int32_t i = 0; i != batch_size; ++i) {
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DecodeOne(p + i * num_frames * vocab_size, num_frames, vocab_size,
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