Merge pull request #13 from alibaba-damo-academy/dev

zhifu gao

2022-12-12 7817db2e202f9790cb6a6e832fd688c01b3be643

Merge pull request #13 from alibaba-damo-academy/dev

Dev

 egs/aishell/paraformer/run.sh

@@ -8,7 +8,7 @@
count=1
gpu_inference=true  # Whether to perform gpu decoding, set false for cpu decoding
# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
njob=8
njob=1
train_cmd=utils/run.pl
infer_cmd=utils/run.pl

@@ -219,7 +219,7 @@
        fi
        ${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
            python -m funasr.bin.asr_inference_launch \
                --batch_size 1 \
                --batch_size 100 \
                --ngpu "${_ngpu}" \
                --njob ${njob} \
                --gpuid_list ${gpuid_list} \

 egs/aishell/paraformerbert/run.sh

@@ -8,7 +8,7 @@
count=1
gpu_inference=true  # Whether to perform gpu decoding, set false for cpu decoding
# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
njob=8
njob=1
train_cmd=utils/run.pl
infer_cmd=utils/run.pl

@@ -235,7 +235,7 @@
        fi
        ${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
            python -m funasr.bin.asr_inference_launch \
                --batch_size 1 \
                --batch_size 100 \
                --ngpu "${_ngpu}" \
                --njob ${njob} \
                --gpuid_list ${gpuid_list} \

 funasr/bin/asr_inference_paraformer.py

@@ -441,7 +441,7 @@
            "decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".
                format(length, forward_time, 100 * forward_time / (length*lfr_factor)))
        
        for batch_id in range(len(results)):
        for batch_id in range(_bs):
            result = [results[batch_id][:-2]]
    
            key = keys[batch_id]

 funasr/models/predictor/cif.py

@@ -31,10 +31,12 @@
        alphas = torch.sigmoid(output)

        alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)

        if mask is not None:

            alphas = alphas * mask.transpose(-1, -2).float()

            mask = mask.transpose(-1, -2).float()

            alphas = alphas * mask

        if mask_chunk_predictor is not None:

            alphas = alphas * mask_chunk_predictor

        alphas = alphas.squeeze(-1)

        mask = mask.squeeze(-1)

        if target_label_length is not None:

            target_length = target_label_length

        elif target_label is not None: