Python kaldi_io.write_mat方法代碼示例

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import write_mat [as 別名]
def _run_kaldi(command, input_type, input_value):
    """Run provided Kaldi command, pass a tensor and get the resulting tensor

    Arguments:
        input_type: str
            'ark' or 'scp'
        input_value:
            Tensor for 'ark'
            string for 'scp' (path to an audio file)
    """
    key = 'foo'
    process = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
    if input_type == 'ark':
        kaldi_io.write_mat(process.stdin, input_value.cpu().numpy(), key=key)
    elif input_type == 'scp':
        process.stdin.write(f'{key} {input_value}'.encode('utf8'))
    else:
        raise NotImplementedError('Unexpected type')
    process.stdin.close()
    result = dict(kaldi_io.read_mat_ark(process.stdout))['foo']
    return torch.from_numpy(result.copy())  # copy supresses some torch warning 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import write_mat [as 別名]
def testPipeReadWrite(self):
        """
        Test read/write for pipes.

        Note: make sure the "os.environ['KALDI_ROOT']" in "kaldi_io/kaldi_io.py" is correct.
        """
        # the following line disables 'stderr' forwarding, comment it for DEBUG,
        with open("/dev/null","w") as sys.stderr:
            # read,
            flt_mat4 = { k:m for k,m in kaldi_io.read_mat_ark('ark:copy-feats ark:tests/data/feats.ark ark:- |') }
            # write to pipe,
            with kaldi_io.open_or_fd('ark:| copy-feats ark:- ark:tests/data_re-saved/mat_pipe.ark','wb') as f:
                for k,m in flt_mat4.items(): kaldi_io.write_mat(f, m, k)
            # read it again and compare,
            for k,m in kaldi_io.read_mat_ark('tests/data_re-saved/mat_pipe.ark'):
                self.assertTrue(np.array_equal(m, flt_mat4[k]),"flt. matrix same after read/write via pipe")

            # read some other formats from pipe,
            i32_vec3 = { k:v for k,v in kaldi_io.read_vec_int_ark('ark:copy-int-vector ark:tests/data/ali.ark ark:- |') }
            flt_vec4 = { k:v for k,v in kaldi_io.read_vec_flt_ark('ark:copy-vector ark:tests/data/conf.ark ark:- |') } 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import write_mat [as 別名]
def forwardXXreverse(args, cpc_model, device, data_loader, output_ark, output_scp):
    logger.info("Starting Forward Passing")
    cpc_model.eval() # not training cdc model 

    ark_scp_output='ark:| copy-feats --compress=true ark:- ark,scp:' + output_ark + ',' + output_scp 

    with torch.no_grad():
        with ko.open_or_fd(ark_scp_output,'wb') as f:
            for [utt_id, data, data_r] in data_loader:
                data   = data.float().unsqueeze(1).to(device) # add channel dimension
                data_r = data_r.float().unsqueeze(1).to(device) # add channel dimension
                data   = data.contiguous()
                data_r = data.contiguous()
                hidden1 = cpc_model.init_hidden1(len(data))
                hidden2 = cpc_model.init_hidden2(len(data))
                output = cpc_model.predict(data, data_r, hidden1, hidden2)
                mat = output.squeeze(0).cpu().numpy() # kaldi io does not accept torch tensor
                ko.write_mat(f, mat, key=utt_id[0]) 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import write_mat [as 別名]
def forward_dct(args, cpc_model, device, data_loader, output_ark, output_scp, dct_dim=24):
    ''' forward with dct '''

    logger.info("Starting Forward Passing")
    cpc_model.eval() # not training cdc model 

    ark_scp_output='ark:| copy-feats --compress=true ark:- ark,scp:' + output_ark + ',' + output_scp 
    with torch.no_grad():
        with ko.open_or_fd(ark_scp_output,'wb') as f:
            for [utt_id, data] in data_loader:
                data = data.float().unsqueeze(1).to(device) # add channel dimension
                data = data.contiguous()
                hidden = cpc_model.init_hidden(len(data))
                output, hidden = cpc_model.predict(data, hidden)
                mat = output.squeeze(0).cpu().numpy() # kaldi io does not accept torch tensor
                dct_mat = fft.dct(mat, type=2, n=dct_dim) # apply dct 
                ko.write_mat(f, dct_mat, key=utt_id[0]) 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import write_mat [as 別名]
def write_kaldi(orig_feat_scp, ark_scp_output, max_len):
    """Write the slice feature matrix to ark_scp_output
    """
    with ko.open_or_fd(ark_scp_output,'wb') as f:
        for key,mat in ko.read_mat_scp(orig_feat_scp):
            tensor = tensor_cnn_utt(mat, max_len)
            if tensor.shape[1] != max_len:
                print(tensor.shape)
            ko.write_mat(f, tensor, key=key) 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import write_mat [as 別名]
def testMatrixReadWrite(self):
        """
        Test read/write for float matrices.
        """
        # read,
        flt_mat = { k:m for k,m in kaldi_io.read_mat_scp('tests/data/feats_ascii.scp') } # ascii-scp,
        flt_mat2 = { k:m for k,m in kaldi_io.read_mat_ark('tests/data/feats_ascii.ark') } # ascii-ark,
        flt_mat3 = { k:m for k,m in kaldi_io.read_mat_ark('tests/data/feats.ark') } # ascii-ark,
        # store,
        with kaldi_io.open_or_fd('tests/data_re-saved/mat.ark','wb') as f:
            for k,m in flt_mat3.items(): kaldi_io.write_mat(f, m, k)
        # read and compare,
        for k,m in kaldi_io.read_mat_ark('tests/data_re-saved/mat.ark'):
            self.assertTrue(np.array_equal(m, flt_mat3[k]), msg="flt. matrix same after re-saving") 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import write_mat [as 別名]
def forward(cpc_model, device, data_loader, output_ark, output_scp):
    logger.info("Starting Forward Passing")
    cpc_model.eval() # not training cdc model 

    ark_scp_output='ark:| copy-feats --compress=true ark:- ark,scp:' + output_ark + ',' + output_scp 

    with torch.no_grad():
        with ko.open_or_fd(ark_scp_output,'wb') as f:
            for [utt_id, data] in data_loader:
                data = data.float().unsqueeze(1).to(device) # add channel dimension
                data = data.contiguous()
                hidden = cpc_model.init_hidden(len(data), use_gpu=False)
                output, hidden = cpc_model.predict(data, hidden)
                mat = output.squeeze(0).cpu().numpy() # kaldi io does not accept torch tensor
                ko.write_mat(f, mat, key=utt_id[0]) 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import write_mat [as 別名]
def main():
    if not os.path.isdir(KALDI_ROOT):
        print('CHANGE THIS TO YOUR OWN KALDI ROOT: ', KALDI_ROOT)
        exit()

    if not os.path.isdir(INPUT_PATH):
        print('Invalid path for the preprocessed timit dataset: ', INPUT_PATH)
        print('Please run \'preprocess_timit.py\' first!')
        exit()
        
    if not os.path.isdir(SOURCE_DIR):
        print('Invalid path for the source directory: ', SOURCE_DIR)
        print('Please read the Wiki page for instructions!')
        exit()

    if not os.path.isdir(OUTPUT_PATH):
        os.mkdir(OUTPUT_PATH)

    # read train and test from the preprocessed directory
    x, ids = [], []
    for s in INPUT_SETS:
        with open(os.path.join(INPUT_PATH, s + '_x.pkl'), 'rb') as fp:
            x += pickle.load(fp)
        with open(os.path.join(INPUT_PATH, s + '_id.pkl'), 'rb') as fp:
            ids += pickle.load(fp)
        assert len(x)==len(ids)
    print('[TIMIT-to-ARK] - ', 'Total Dataset len:', len(x))

    # construct all input dict
    all_inputs = {}
    for idx, i in enumerate(ids):
        i = str(i).strip('.wav').split('/')
        i = i[-2].upper() + '_' + i[-1].upper()
        all_inputs[i] = np.asarray(x[idx])

    # filter all input with kaldi generated files
    for s in OUTPUT_SETS:
        if not os.path.isdir(SOURCE_DIR):
            raise NotADirectoryError('Source directory does not exist!', SOURCE_DIR)    
        
        if not os.path.isdir(OUTPUT_PATH + '/' + str(s)):
            os.mkdir(OUTPUT_PATH + '/' + str(s))
        
        # read train / dev / test from the kaldi generated directory
        partial_outputs = {}
        with open(os.path.join(SOURCE_DIR, s + '/feats.scp'), 'r') as f:
            lines = f.readlines()
            for line in lines:
                line = line.split(' ')[0]
                if line in all_inputs:
                    partial_outputs[line] = all_inputs[line]
            assert len(lines) == len(partial_outputs)

        # writiing output with kaldi_io
        ark_scp_output = 'ark:| copy-feats --compress=true ark:- ark,scp:{}/raw_mel_{}.ark,{}/{}/feats.scp'.format(OUTPUT_PATH, str(s), OUTPUT_PATH, str(s))
        with kaldi_io.open_or_fd(ark_scp_output, 'wb') as f:
            for key, mat in tqdm(partial_outputs.items()): 
                kaldi_io.write_mat(f, mat, key=key)

    print('[TIMIT-to-ARK] - All done, saved at \'' + str(OUTPUT_PATH) + '\' exit.')