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support wav file input

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haoneng.lhn 2023-04-28 11:22:24 +08:00
parent 77232f8801
commit dcc4053d3e
1 changed files with 27 additions and 5 deletions
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32
funasr/bin/asr_inference_paraformer_streaming.py
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@ -20,6 +20,7 @@ from typing import List
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 @@ def inference_modelscope(
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 @@ def inference_modelscope(
# 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)