support wav file input

haoneng.lhn

2023-04-28 dcc4053d3e25ff3962e9a8236a26ef69ae4c4827

support wav file input

 funasr/bin/asr_inference_paraformer_streaming.py

@@ -20,6 +20,7 @@

import numpy as np
import torch
import torchaudio
from typeguard import check_argument_types

from funasr.fileio.datadir_writer import DatadirWriter
@@ -515,6 +516,8 @@
        if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "bytes":
            raw_inputs = _load_bytes(data_path_and_name_and_type[0])
            raw_inputs = torch.tensor(raw_inputs)
        if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "sound":
            raw_inputs = torchaudio.load(data_path_and_name_and_type[0])[0][0]
        if data_path_and_name_and_type is None and raw_inputs is not None:
            if isinstance(raw_inputs, np.ndarray):
                raw_inputs = torch.tensor(raw_inputs)
@@ -531,13 +534,32 @@
        # 7 .Start for-loop
        # FIXME(kamo): The output format should be discussed about
        raw_inputs = torch.unsqueeze(raw_inputs, axis=0)
        input_lens = torch.tensor([raw_inputs.shape[1]])
        asr_result_list = []

        cache = _prepare_cache(cache, chunk_size=chunk_size, batch_size=1)
        cache["encoder"]["is_final"] = is_final
        asr_result = speech2text(cache, raw_inputs, input_lens)
        item = {'key': "utt", 'value': asr_result}
        item = {}
        if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "sound":
            sample_offset = 0
            speech_length = raw_inputs.shape[1]
            stride_size =  chunk_size[1] * 960
            cache = _prepare_cache(cache, chunk_size=chunk_size, batch_size=1)
            final_result = ""
            for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
                if sample_offset + stride_size >= speech_length - 1:
                    stride_size = speech_length - sample_offset
                    cache["encoder"]["is_final"] = True
                else:
                    cache["encoder"]["is_final"] = False
                input_lens = torch.tensor([stride_size])
                asr_result = speech2text(cache, raw_inputs[:, sample_offset: sample_offset + stride_size], input_lens)
                if len(asr_result) != 0: 
                    final_result += asr_result[0]
            item = {'key': "utt", 'value': [final_result]}
        else:
            input_lens = torch.tensor([raw_inputs.shape[1]])
            cache["encoder"]["is_final"] = is_final
            asr_result = speech2text(cache, raw_inputs, input_lens)
            item = {'key': "utt", 'value': asr_result}

        asr_result_list.append(item)
        if is_final:
            cache = _cache_reset(cache, chunk_size=chunk_size, batch_size=1)