Python kaldi_io.open_or_fd方法代碼示例

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import open_or_fd [as 別名]
def testFloatVectorReadWrite(self):
        """
        Test read/write for float vectors.
        """
        # read,
        flt_vec = { k:v for k,v in kaldi_io.read_vec_flt_scp('tests/data/conf.scp') } # scp,
        return

        flt_vec2 = { k:v for k,v in kaldi_io.read_vec_flt_ark('tests/data/conf.ark') } # binary-ark,
        flt_vec3 = { k:v for k,v in kaldi_io.read_vec_flt_ark('tests/data/conf_ascii.ark') } # ascii-ark,
        # store,
        with kaldi_io.open_or_fd('tests/data_re-saved/conf.ark','wb') as f:
            for k,v in flt_vec.items(): kaldi_io.write_vec_flt(f, v, k)
        # read and compare,
        for k,v in kaldi_io.read_vec_flt_ark('tests/data_re-saved/conf.ark'):
            self.assertTrue(np.array_equal(v,flt_vec[k]), msg="flt. vector same after re-saving") 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import open_or_fd [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 open_or_fd [as 別名]
def testWriteReadPosteriors(self):
        data = [[(0, 0.0), (1, 0.1), (2, 0.2)],
                [(0, 0.00), (1, 0.11), (2, 0.22)],
                [(0, 0.000), (1, 0.111), (3, 0.333)]]
        key = 'posterior_test1'
        with kaldi_io.open_or_fd('tests/data_re-saved/posterior_tests.ark','wb') as w:
            kaldi_io.write_post(w, data, key=key)

        with kaldi_io.open_or_fd('tests/data_re-saved/posterior_tests.ark', 'rb') as r:
            posts = [(k, posteriors) for k, posteriors in kaldi_io.read_post_ark(r)]
            self.assertEqual(len(posts), 1)
            self.assertEqual(posts[0][0], key)
            rdata = posts[0][1]
            self.assertEqual(len(rdata), len(data))
            for a1, a2 in zip(rdata, data):
                self.assertEqual(len(a1), len(a2))
                for ((idx1, p1), (idx, p)) in zip(a1, a2):
                    self.assertEqual(idx1, idx)
                    self.assertAlmostEqual(p1, p)


# if stand-alone, run this... 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import open_or_fd [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 open_or_fd [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 open_or_fd [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 open_or_fd [as 別名]
def read_all_key(file):
    """return all keys/utterances of a kaldi scp file
    """
    key_list = []
    fd = ko.open_or_fd(file)
    try:
        for line in fd:
            (key,_) = line.decode().split(' ')
            key_list.append(key)
    finally:
        if fd is not file: fd.close()
        return key_list 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import open_or_fd [as 別名]
def read_mat_key(file, target_key):
    """read the matrix of the target key/utterance from a kaldi scp file
    """
    fd = ko.open_or_fd(file)
    try:
        for line in fd:
            (key,rxfile) = line.decode().split(' ')
            if key == target_key:
                return ko.read_mat(rxfile)
    finally:
        if fd is not file: fd.close() 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import open_or_fd [as 別名]
def __execute_command__(self, datain, cmd):
        #try:
        fin, fout = kio.open_or_fd(cmd, 'wb')
        kio.write_wav(fin, datain, self.sr, key='utt')
        fin.close() #so its clear nothing new arrives
        feats_ark = kio.read_mat_ark(fout)
        for _, feats in feats_ark:
            fout.close()
            return feats.T #there is only one to read
        #except Exception as e:
        #    print (e)
        #    return None 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import open_or_fd [as 別名]
def testInt32VectorReadWrite(self):
        """
        Test read/write for int32 vectors.
        """
        # read,
        i32_vec = { k:v for k,v in kaldi_io.read_vec_int_ark('tests/data/ali.ark') } # binary,
        i32_vec2 = { k:v for k,v in kaldi_io.read_vec_int_ark('tests/data/ali_ascii.ark') } # ascii,
        # re-save the data,
        with kaldi_io.open_or_fd('tests/data_re-saved/ali.ark','wb') as f:
            for k,v in i32_vec.items(): kaldi_io.write_vec_int(f, v, k)
        # read and make sure it is the same,
        for k,v in kaldi_io.read_vec_int_ark('tests/data_re-saved/ali.ark'):
            self.assertTrue(np.array_equal(v,i32_vec[k]), msg="int32 vector same after re-saving") 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import open_or_fd [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 open_or_fd [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 open_or_fd [as 別名]
def eval_dnn(args):
    """ The main function for doing evaluation on a trained network.

    Args:
        args: a Namespace object with the required parameters
            obtained from the function process_args()
    """

    model_dir = args.model_dir
    use_gpu = args.use_gpu == 'yes'
    min_chunk_size = args.min_chunk_size
    chunk_size = args.chunk_size
    # First change the output files temp ones and at the end rename them
    wspecifier, ark, scp = process_wspecifier(args.vector_wspecifier)

    if ark is not None and os.path.exists(ark) and scp is not None and os.path.exists(scp):
        logger.info('Both output ark and scp files exist. Return from this call.')
        return
    model = Model()
    with kaldi_io.open_or_fd(args.feature_rspecifier) as input_fid:
        with kaldi_io.open_or_fd(wspecifier) as output_fid:
            model.make_embedding(input_fid, output_fid, model_dir, min_chunk_size, chunk_size, use_gpu, logger)

    # rename output files
    if ark is not None:
        os.rename(ark + '.tmp.ark', ark)
    # first load scp and correct them to point to renamed ark file.
    if scp is not None:
        with open(scp + '.tmp.scp', 'rt') as fid_in:
            with open(scp + '.tmp', 'wr') as fid_out:
                text = fid_in.read()
                text = text.replace('ark.tmp.ark', 'ark')
                # Sometimes there is no \n at the end of file ank cause a Kaldi error.
                # For preventing this error juts check the last char and append \n if not exist
                if text[-1] != '\n':
                    text += '\n'
                fid_out.write(text)
        os.rename(scp + '.tmp', scp)
        # after create scp file  now we can delete temp file
        # os.remove(scp + '.tmp.scp') 

# 需要導入模塊: import kaldi_io [as 別名]
# 或者: from kaldi_io import open_or_fd [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.')