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@ -805,7 +805,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
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MEM_REQ_SCRATCH3.at (model.type) +
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scale*MEM_REQ_MODEL.at (model.type) +
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scale*MEM_REQ_KV_CROSS.at(model.type) +
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scale*std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type));
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scale*std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type));
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// this is the memory required by one decoder
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const size_t mem_required_decoder =
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@ -2962,6 +2962,9 @@ struct whisper_full_params whisper_full_default_params(enum whisper_sampling_str
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/*.encoder_begin_callback =*/ nullptr,
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/*.encoder_begin_callback_user_data =*/ nullptr,
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/*.logits_filter_callback =*/ nullptr,
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/*.logits_filter_callback_user_data =*/ nullptr,
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};
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switch (strategy) {
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@ -3089,7 +3092,7 @@ static const std::vector<std::string> non_speech_tokens = {
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// - applies logit filters
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// - computes logprobs and probs
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static void whisper_process_logits(
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const struct whisper_context & ctx,
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struct whisper_context & ctx,
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const struct whisper_full_params params,
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struct whisper_decoder & decoder,
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float temperature) {
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@ -3145,6 +3148,9 @@ static void whisper_process_logits(
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logits[vocab.token_translate] = -INFINITY;
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logits[vocab.token_transcribe] = -INFINITY;
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if (params.logits_filter_callback) {
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params.logits_filter_callback(&ctx, tokens_cur.data(), tokens_cur.size(), logits.data(), params.logits_filter_callback_user_data);
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}
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// suppress non-speech tokens
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// ref: https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/tokenizer.py#L224-L253
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@ -3848,7 +3854,7 @@ int whisper_full(
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return a.sequence.sum_logprobs_all > b.sequence.sum_logprobs_all;
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});
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unsigned int cur_c = 0;
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uint32_t cur_c = 0;
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for (int j = 0; j < n_decoders_cur; ++j) {
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auto & decoder = ctx->decoders[j];
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