egs/aishell/conformer/conf/train_asr_conformer.yaml
@@ -83,6 +83,8 @@ num_time_mask: 2 dataset_conf: data_names: speech,text data_types: sound,text shuffle: True shuffle_conf: shuffle_size: 2048 @@ -93,4 +95,4 @@ num_workers: 8 log_interval: 50 normalize: None normalize: None egs/aishell/conformer/run.sh
@@ -135,6 +135,7 @@ --data_dir ${feats_dir}/data \ --train_set ${train_set} \ --valid_set ${valid_set} \ --data_file_names "wav.scp,text" \ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \ --speed_perturb ${speed_perturb} \ --resume true \ egs/aishell/data2vec_paraformer_finetune/conf/train_asr_paraformer_transformer_12e_6d_3072_768.yaml
@@ -105,6 +105,8 @@ r_order: 1 dataset_conf: data_names: speech,text data_types: sound,text shuffle: True shuffle_conf: shuffle_size: 2048 egs/aishell/data2vec_paraformer_finetune/run.sh
@@ -139,6 +139,7 @@ --data_dir ${feats_dir}/data \ --train_set ${train_set} \ --valid_set ${valid_set} \ --data_file_names "wav.scp,text" \ --init_param ${init_param} \ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \ --resume true \ egs/aishell/data2vec_transformer_finetune/conf/train_asr_transformer_12e_6d_3072_768.yaml
@@ -96,6 +96,8 @@ num_time_mask: 2 dataset_conf: data_names: speech,text data_types: sound,text shuffle: True shuffle_conf: shuffle_size: 2048 egs/aishell/data2vec_transformer_finetune/run.sh
@@ -139,6 +139,7 @@ --data_dir ${feats_dir}/data \ --train_set ${train_set} \ --valid_set ${valid_set} \ --data_file_names "wav.scp,text" \ --init_param ${init_param} \ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \ --speed_perturb ${speed_perturb} \ egs/aishell/paraformer/conf/train_asr_paraformer_conformer_12e_6d_2048_256.yaml
@@ -93,6 +93,8 @@ tail_threshold: 0.45 dataset_conf: data_names: speech,text data_types: sound,text shuffle: True shuffle_conf: shuffle_size: 2048 egs/aishell/paraformer/run.sh
@@ -135,6 +135,7 @@ --data_dir ${feats_dir}/data \ --train_set ${train_set} \ --valid_set ${valid_set} \ --data_file_names "wav.scp,text" \ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \ --speed_perturb ${speed_perturb} \ --resume true \ egs/aishell/paraformerbert/local/extract_embeds.sh
@@ -54,6 +54,8 @@ cat ${local_records_dir}/embeds.${JOB}.shape || exit 1; done > ${local_scp_dir_raw}/embeds.shape fi cp ${local_scp_dir_raw}/embeds.scp ${raw_dataset_path}/data/${data_set}/embeds.scp done echo "embeds is in: ${local_scp_dir_raw}" egs/aishell/paraformerbert/run.sh
@@ -146,7 +146,7 @@ --data_dir ${feats_dir}/data \ --train_set ${train_set} \ --valid_set ${valid_set} \ --embed_path ${feats_dir}/data \ --data_file_names "wav.scp,text,embed.scp" \ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \ --speed_perturb ${speed_perturb} \ --resume true \ egs/aishell/transformer/conf/train_asr_transformer.yaml
@@ -73,6 +73,8 @@ warmup_steps: 25000 dataset_conf: data_names: speech,text data_types: sound,text shuffle: True shuffle_conf: shuffle_size: 2048 egs/aishell/transformer/run.sh
@@ -135,6 +135,7 @@ --data_dir ${feats_dir}/data \ --train_set ${train_set} \ --valid_set ${valid_set} \ --data_file_names "wav.scp,text" \ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \ --speed_perturb ${speed_perturb} \ --resume true \ egs/aishell2/conformer/conf/train_asr_conformer.yaml
@@ -84,6 +84,7 @@ num_time_mask: 2 dataset_conf: data_names: speech,text data_types: sound,text shuffle: True shuffle_conf: egs/aishell2/conformer/run.sh
@@ -103,8 +103,6 @@ utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/${train_set}/text | cut -f 2- -d" " | tr " " "\n" \ | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list} echo "<unk>" >> ${token_list} mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${train_set} mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${valid_set} fi # LM Training Stage @@ -139,6 +137,7 @@ --data_dir ${feats_dir}/data \ --train_set ${train_set} \ --valid_set ${valid_set} \ --data_file_names "wav.scp,text" \ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \ --speed_perturb ${speed_perturb} \ --dataset_type $dataset_type \ egs/aishell2/data2vec_pretrain/conf/train_pretrain_transformer.yaml
@@ -72,8 +72,8 @@ # for dataset dataset_conf: batch_mode: clipping data_names: speech,none data_types: sound,none data_names: speech data_types: sound shuffle: true shuffle_conf: shuffle_size: 12800 egs/aishell2/data2vec_pretrain/run.sh
@@ -82,8 +82,6 @@ utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/${train_set}/text | cut -f 2- -d" " | tr " " "\n" \ | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list} echo "<unk>" >> ${token_list} mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${train_set} mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${valid_set} fi # Training Stage @@ -110,6 +108,7 @@ --data_dir ${feats_dir}/data \ --train_set ${train_set} \ --valid_set ${valid_set} \ --data_file_names "wav.scp" \ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \ --speed_perturb ${speed_perturb} \ --dataset_type $dataset_type \ egs/aishell2/paraformer/conf/train_asr_paraformer_conformer_20e_1280_320_6d_1280_320.yaml
@@ -94,6 +94,7 @@ r_order: 1 dataset_conf: data_names: speech,text data_types: sound,text shuffle: True shuffle_conf: egs/aishell2/paraformer/run.sh
@@ -137,6 +137,7 @@ --data_dir ${feats_dir}/data \ --train_set ${train_set} \ --valid_set ${valid_set} \ --data_file_names "wav.scp,text" \ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \ --speed_perturb ${speed_perturb} \ --dataset_type $dataset_type \ egs/aishell2/paraformerbert/local/extract_embeds.sh
@@ -54,6 +54,8 @@ cat ${local_records_dir}/embeds.${JOB}.shape || exit 1; done > ${local_scp_dir_raw}/embeds.shape fi cp ${local_scp_dir_raw}/embeds.scp ${raw_dataset_path}/data/${data_set}/embeds.scp done echo "embeds is in: ${local_scp_dir_raw}" egs/aishell2/paraformerbert/run.sh
@@ -147,7 +147,7 @@ --data_dir ${feats_dir}/data \ --train_set ${train_set} \ --valid_set ${valid_set} \ --embed_path ${feats_dir}/data \ --data_file_names "wav.scp,text,embed.scp" \ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \ --speed_perturb ${speed_perturb} \ --dataset_type $dataset_type \ egs/aishell2/transformer/conf/train_asr_transformer.yaml
@@ -78,6 +78,7 @@ num_time_mask: 2 dataset_conf: data_names: speech,text data_types: sound,text shuffle: True shuffle_conf: egs/aishell2/transformer/run.sh
@@ -137,6 +137,7 @@ --data_dir ${feats_dir}/data \ --train_set ${train_set} \ --valid_set ${valid_set} \ --data_file_names "wav.scp,text" \ --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \ --speed_perturb ${speed_perturb} \ --dataset_type $dataset_type \ egs/alimeeting/sa-asr/conf/train_sa_asr_conformer.yaml
@@ -43,7 +43,6 @@ pooling_type: statistic num_nodes_resnet1: 256 num_nodes_last_layer: 256 batchnorm_momentum: 0.5 # decoder related decoder: sa_decoder egs/librispeech/conformer/run.sh
@@ -55,7 +55,7 @@ inference_config=conf/decode_asr_transformer.yaml #inference_config=conf/decode_asr_transformer_beam60_ctc0.3.yaml inference_asr_model=valid.acc.ave_10best.pth inference_asr_model=valid.acc.ave_10best.pb # you can set gpu num for decoding here gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default egs/librispeech_100h/conformer/run.sh
@@ -55,7 +55,7 @@ inference_config=conf/decode_asr_transformer.yaml #inference_config=conf/decode_asr_transformer_beam60_ctc0.3.yaml inference_asr_model=valid.acc.ave_10best.pth inference_asr_model=valid.acc.ave_10best.pb # you can set gpu num for decoding here gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default egs_modelscope/speaker_diarization/speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch/infer.py
@@ -7,8 +7,9 @@ from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks # 初始化推理 pipeline # 当以原始音频作为输入时使用配置文件 sond.yaml，并设置 mode 为sond_demo # initialize the pipeline for inference # when using the raw waveform files to inference, please use the config file `sond.yaml` # and set mode to `sond_demo` inference_diar_pipline = pipeline( mode="sond_demo", num_workers=0, @@ -19,7 +20,8 @@ sv_model_revision="master", ) # 以 audio_list 作为输入，其中第一个音频为待检测语音，后面的音频为不同说话人的声纹注册语音 # use audio_list as the input, where the first one is the record to be detected # and the following files are enrollments for different speakers audio_list = [ "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/record.wav", "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_A.wav", egs_modelscope/speaker_diarization/speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/infer.py
@@ -7,8 +7,9 @@ from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks # 初始化推理 pipeline # 当以原始音频作为输入时使用配置文件 sond.yaml，并设置 mode 为sond_demo # initialize the pipeline for inference # when using the raw waveform files to inference, please use the config file `sond.yaml` # and set mode to `sond_demo` inference_diar_pipline = pipeline( mode="sond_demo", num_workers=0, @@ -19,7 +20,8 @@ sv_model_revision="master", ) # 以 audio_list 作为输入，其中第一个音频为待检测语音，后面的音频为不同说话人的声纹注册语音 # use audio_list as the input, where the first one is the record to be detected # and the following files are enrollments for different speakers audio_list = [ "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/speaker_diarization/record.wav", "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/speaker_diarization/spk1.wav", egs_modelscope/speaker_verification/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/infer_sv.py
@@ -7,13 +7,13 @@ model='damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch' ) # 两个语音为相同说话人 # the same speaker rec_result = inference_sv_pipline(audio_in=( 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav', 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_same.wav')) print("Similarity", rec_result["scores"]) # 两个语音为不同说话人 # different speaker rec_result = inference_sv_pipline(audio_in=( 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav', 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_different.wav')) funasr/bin/asr_infer.py
@@ -762,23 +762,6 @@ feats_len = speech_lengths if feats.shape[1] != 0: if cache_en["is_final"]: if feats.shape[1] + cache_en["chunk_size"][2] < cache_en["chunk_size"][1]: cache_en["last_chunk"] = True else: # first chunk feats_chunk1 = feats[:, :cache_en["chunk_size"][1], :] feats_len = torch.tensor([feats_chunk1.shape[1]]) results_chunk1 = self.infer(feats_chunk1, feats_len, cache) # last chunk cache_en["last_chunk"] = True feats_chunk2 = feats[:, -(feats.shape[1] + cache_en["chunk_size"][2] - cache_en["chunk_size"][1]):, :] feats_len = torch.tensor([feats_chunk2.shape[1]]) results_chunk2 = self.infer(feats_chunk2, feats_len, cache) return [" ".join(results_chunk1 + results_chunk2)] results = self.infer(feats, feats_len, cache) return results funasr/bin/diar_inference_launch.py
@@ -38,7 +38,6 @@ from scipy.signal import medfilt from funasr.utils.cli_utils import get_commandline_args from funasr.tasks.diar import DiarTask from funasr.tasks.asr import ASRTask from funasr.tasks.diar import EENDOLADiarTask from funasr.torch_utils.device_funcs import to_device from funasr.torch_utils.set_all_random_seed import set_all_random_seed @@ -187,7 +186,7 @@ raise TypeError("raw_inputs must be a list or tuple in [speech, profile1, profile2, ...] ") else: # 3. Build data-iterator loader = ASRTask.build_streaming_iterator( loader = DiarTask.build_streaming_iterator( data_path_and_name_and_type, dtype=dtype, batch_size=batch_size, funasr/bin/sv_infer.py
@@ -23,7 +23,6 @@ from funasr.utils.cli_utils import get_commandline_args from funasr.tasks.sv import SVTask from funasr.tasks.asr import ASRTask from funasr.torch_utils.device_funcs import to_device from funasr.torch_utils.set_all_random_seed import set_all_random_seed from funasr.utils import config_argparse funasr/bin/sv_inference_launch.py
@@ -34,7 +34,6 @@ from funasr.utils.cli_utils import get_commandline_args from funasr.tasks.sv import SVTask from funasr.tasks.asr import ASRTask from funasr.torch_utils.device_funcs import to_device from funasr.torch_utils.set_all_random_seed import set_all_random_seed from funasr.utils import config_argparse @@ -115,7 +114,7 @@ data_path_and_name_and_type = [raw_inputs, "speech", "waveform"] # 3. Build data-iterator loader = ASRTask.build_streaming_iterator( loader = SVTask.build_streaming_iterator( data_path_and_name_and_type, dtype=dtype, batch_size=batch_size, funasr/bin/train.py
@@ -335,6 +335,12 @@ help="dev dataset", ) parser.add_argument( "--data_file_names", type=str, default="wav.scp,text", help="input data files", ) parser.add_argument( "--speed_perturb", type=float, nargs="+", funasr/models/encoder/sanm_encoder.py
@@ -355,18 +355,9 @@ def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}): if len(cache) == 0: return feats # process last chunk cache["feats"] = to_device(cache["feats"], device=feats.device) overlap_feats = torch.cat((cache["feats"], feats), dim=1) if cache["is_final"]: cache["feats"] = overlap_feats[:, -cache["chunk_size"][0]:, :] if not cache["last_chunk"]: padding_length = sum(cache["chunk_size"]) - overlap_feats.shape[1] overlap_feats = overlap_feats.transpose(1, 2) overlap_feats = F.pad(overlap_feats, (0, padding_length)) overlap_feats = overlap_feats.transpose(1, 2) else: cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :] cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :] return overlap_feats def forward_chunk(self, funasr/models/predictor/cif.py
@@ -221,13 +221,14 @@ if cache is not None and "chunk_size" in cache: alphas[:, :cache["chunk_size"][0]] = 0.0 alphas[:, sum(cache["chunk_size"][:2]):] = 0.0 if "is_final" in cache and not cache["is_final"]: alphas[:, sum(cache["chunk_size"][:2]):] = 0.0 if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache: cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device) cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device) hidden = torch.cat((cache["cif_hidden"], hidden), dim=1) alphas = torch.cat((cache["cif_alphas"], alphas), dim=1) if cache is not None and "last_chunk" in cache and cache["last_chunk"]: if cache is not None and "is_final" in cache and cache["is_final"]: tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device) tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device) tail_alphas = torch.tile(tail_alphas, (batch_size, 1)) funasr/utils/prepare_data.py
@@ -3,6 +3,7 @@ import shutil from multiprocessing import Pool import kaldiio import numpy as np import torch.distributed as dist import torchaudio @@ -48,49 +49,80 @@ def calc_shape_core(root_path, args, idx): wav_scp_file = os.path.join(root_path, "wav.scp.{}".format(idx)) shape_file = os.path.join(root_path, "speech_shape.{}".format(idx)) with open(wav_scp_file) as f: file_name = args.data_file_names.split(",")[0] data_name = args.dataset_conf.get("data_names", "speech,text").split(",")[0] scp_file = os.path.join(root_path, "{}.{}".format(file_name, idx)) shape_file = os.path.join(root_path, "{}_shape.{}".format(data_name, idx)) with open(scp_file) as f: lines = f.readlines() frontend_conf = args.frontend_conf dataset_conf = args.dataset_conf speech_length_min = dataset_conf.speech_length_min if hasattr(dataset_conf, "speech_length_min") else -1 speech_length_max = dataset_conf.speech_length_max if hasattr(dataset_conf, "speech_length_max") else -1 with open(shape_file, "w") as f: for line in lines: sample_name, wav_path = line.strip().split() n_frames, feature_dim = wav2num_frame(wav_path, frontend_conf) write_flag = True if n_frames > 0 and speech_length_min > 0: write_flag = n_frames >= speech_length_min if n_frames > 0 and speech_length_max > 0: write_flag = n_frames <= speech_length_max if write_flag: f.write("{} {},{}\n".format(sample_name, str(int(np.ceil(n_frames))), str(int(feature_dim)))) data_type = args.dataset_conf.get("data_types", "sound,text").split(",")[0] if data_type == "sound": frontend_conf = args.frontend_conf dataset_conf = args.dataset_conf length_min = dataset_conf.speech_length_min if hasattr(dataset_conf, "{}_length_min".format(data_name)) else -1 length_max = dataset_conf.speech_length_max if hasattr(dataset_conf, "{}_length_max".format(data_name)) else -1 with open(shape_file, "w") as f: for line in lines: sample_name, wav_path = line.strip().split() n_frames, feature_dim = wav2num_frame(wav_path, frontend_conf) write_flag = True if n_frames > 0 and length_min > 0: write_flag = n_frames >= length_min if n_frames > 0 and length_max > 0: write_flag = n_frames <= length_max if write_flag: f.write("{} {},{}\n".format(sample_name, str(int(np.ceil(n_frames))), str(int(feature_dim)))) f.flush() elif data_type == "kaldi_ark": dataset_conf = args.dataset_conf length_min = dataset_conf.speech_length_min if hasattr(dataset_conf, "{}_length_min".format(data_name)) else -1 length_max = dataset_conf.speech_length_max if hasattr(dataset_conf, "{}_length_max".format(data_name)) else -1 with open(shape_file, "w") as f: for line in lines: sample_name, feature_path = line.strip().split() feature = kaldiio.load_mat(feature_path) n_frames, feature_dim = feature.shape if n_frames > 0 and length_min > 0: write_flag = n_frames >= length_min if n_frames > 0 and length_max > 0: write_flag = n_frames <= length_max if write_flag: f.write("{} {},{}\n".format(sample_name, str(int(np.ceil(n_frames))), str(int(feature_dim)))) f.flush() elif data_type == "text": with open(shape_file, "w") as f: for line in lines: sample_name, text = line.strip().split(maxsplit=1) n_tokens = len(text.split()) f.write("{} {}\n".format(sample_name, str(int(np.ceil(n_tokens))))) f.flush() else: raise RuntimeError("Unsupported data_type: {}".format(data_type)) def calc_shape(args, dataset, nj=64): shape_path = os.path.join(args.data_dir, dataset, "speech_shape") data_name = args.dataset_conf.get("data_names", "speech,text").split(",")[0] shape_path = os.path.join(args.data_dir, dataset, "{}_shape".format(data_name)) if os.path.exists(shape_path): logging.info('Shape file for small dataset already exists.') return split_shape_path = os.path.join(args.data_dir, dataset, "shape_files") split_shape_path = os.path.join(args.data_dir, dataset, "{}_shape_files".format(data_name)) if os.path.exists(split_shape_path): shutil.rmtree(split_shape_path) os.mkdir(split_shape_path) # split wav_scp_file = os.path.join(args.data_dir, dataset, "wav.scp") with open(wav_scp_file) as f: file_name = args.data_file_names.split(",")[0] scp_file = os.path.join(args.data_dir, dataset, file_name) with open(scp_file) as f: lines = f.readlines() num_lines = len(lines) num_job_lines = num_lines // nj start = 0 for i in range(nj): end = start + num_job_lines file = os.path.join(split_shape_path, "wav.scp.{}".format(str(i + 1))) file = os.path.join(split_shape_path, "{}.{}".format(file_name, str(i + 1))) with open(file, "w") as f: if i == nj - 1: f.writelines(lines[start:]) @@ -108,15 +140,18 @@ # combine with open(shape_path, "w") as f: for i in range(nj): job_file = os.path.join(split_shape_path, "speech_shape.{}".format(str(i + 1))) job_file = os.path.join(split_shape_path, "{}_shape.{}".format(data_name, str(i + 1))) with open(job_file) as job_f: lines = job_f.readlines() f.writelines(lines) logging.info('Generating shape files done.') def generate_data_list(data_dir, dataset, nj=64): list_file = os.path.join(data_dir, dataset, "data.list") def generate_data_list(args, data_dir, dataset, nj=64): data_names = args.dataset_conf.get("data_names", "speech,text").split(",") file_names = args.data_file_names.split(",") concat_data_name = "_".join(data_names) list_file = os.path.join(data_dir, dataset, "{}_data.list".format(concat_data_name)) if os.path.exists(list_file): logging.info('Data list for large dataset already exists.') return @@ -125,85 +160,67 @@ shutil.rmtree(split_path) os.mkdir(split_path) with open(os.path.join(data_dir, dataset, "wav.scp")) as f_wav: wav_lines = f_wav.readlines() with open(os.path.join(data_dir, dataset, "text")) as f_text: text_lines = f_text.readlines() num_lines = len(wav_lines) data_lines_list = [] for file_name in file_names: with open(os.path.join(data_dir, dataset, file_name)) as f: lines = f.readlines() data_lines_list.append(lines) num_lines = len(data_lines_list[0]) num_job_lines = num_lines // nj start = 0 for i in range(nj): end = start + num_job_lines split_path_nj = os.path.join(split_path, str(i + 1)) os.mkdir(split_path_nj) wav_file = os.path.join(split_path_nj, "wav.scp") text_file = os.path.join(split_path_nj, "text") with open(wav_file, "w") as fw, open(text_file, "w") as ft: if i == nj - 1: fw.writelines(wav_lines[start:]) ft.writelines(text_lines[start:]) else: fw.writelines(wav_lines[start:end]) ft.writelines(text_lines[start:end]) for file_id, file_name in enumerate(file_names): file = os.path.join(split_path_nj, file_name) with open(file, "w") as f: if i == nj - 1: f.writelines(data_lines_list[file_id][start:]) else: f.writelines(data_lines_list[file_id][start:end]) start = end with open(list_file, "w") as f_data: for i in range(nj): wav_path = os.path.join(split_path, str(i + 1), "wav.scp") text_path = os.path.join(split_path, str(i + 1), "text") f_data.write(wav_path + " " + text_path + "\n") path = "" for file_name in file_names: path = path + os.path.join(split_path, str(i + 1), file_name) f_data.write(path + "\n") def prepare_data(args, distributed_option): distributed = distributed_option.distributed if not distributed or distributed_option.dist_rank == 0: filter_wav_text(args.data_dir, args.train_set) filter_wav_text(args.data_dir, args.valid_set) if hasattr(args, "filter_input") and args.filter_input: filter_wav_text(args.data_dir, args.train_set) filter_wav_text(args.data_dir, args.valid_set) if args.dataset_type == "small": calc_shape(args, args.train_set) calc_shape(args, args.valid_set) if args.dataset_type == "large": generate_data_list(args.data_dir, args.train_set) generate_data_list(args.data_dir, args.valid_set) generate_data_list(args, args.data_dir, args.train_set) generate_data_list(args, args.data_dir, args.valid_set) data_names = args.dataset_conf.get("data_names", "speech,text").split(",") data_types = args.dataset_conf.get("data_types", "sound,text").split(",") file_names = args.data_file_names.split(",") print("data_names: {}, data_types: {}, file_names: {}".format(data_names, data_types, file_names)) assert len(data_names) == len(data_types) == len(file_names) if args.dataset_type == "small": args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "speech_shape")] args.valid_shape_file = [os.path.join(args.data_dir, args.valid_set, "speech_shape")] data_names = args.dataset_conf.get("data_names", "speech,text").split(",") data_types = args.dataset_conf.get("data_types", "sound,text").split(",") args.train_data_path_and_name_and_type = [ ["{}/{}/wav.scp".format(args.data_dir, args.train_set), data_names[0], data_types[0]], ["{}/{}/text".format(args.data_dir, args.train_set), data_names[1], data_types[1]] ] args.valid_data_path_and_name_and_type = [ ["{}/{}/wav.scp".format(args.data_dir, args.valid_set), data_names[0], data_types[0]], ["{}/{}/text".format(args.data_dir, args.valid_set), data_names[1], data_types[1]] ] if args.embed_path is not None: args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "{}_shape".format(data_names[0]))] args.valid_shape_file = [os.path.join(args.data_dir, args.valid_set, "{}_shape".format(data_names[0]))] args.train_data_path_and_name_and_type, args.valid_data_path_and_name_and_type = [], [] for file_name, data_name, data_type in zip(file_names, data_names, data_types): args.train_data_path_and_name_and_type.append( [os.path.join(args.embed_path, "embeds", args.train_set, "embeds.scp"), "embed", "kaldi_ark"]) ["{}/{}/{}".format(args.data_dir, args.train_set, file_name), data_name, data_type]) args.valid_data_path_and_name_and_type.append( [os.path.join(args.embed_path, "embeds", args.valid_set, "embeds.scp"), "embed", "kaldi_ark"]) ["{}/{}/{}".format(args.data_dir, args.valid_set, file_name), data_name, data_type]) else: args.train_data_file = os.path.join(args.data_dir, args.train_set, "data.list") args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "data.list") if args.embed_path is not None: if not distributed or distributed_option.dist_rank == 0: for d in [args.train_set, args.valid_set]: file = os.path.join(args.data_dir, d, "data.list") with open(file) as f: lines = f.readlines() out_file = os.path.join(args.data_dir, d, "data_with_embed.list") with open(out_file, "w") as out_f: for line in lines: parts = line.strip().split() idx = parts[0].split("/")[-2] embed_file = os.path.join(args.embed_path, "embeds", args.valid_set, "ark", "embeds.{}.ark".format(idx)) out_f.write(parts[0] + " " + parts[1] + " " + embed_file + "\n") args.train_data_file = os.path.join(args.data_dir, args.train_set, "data_with_embed.list") args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "data_with_embed.list") concat_data_name = "_".join(data_names) args.train_data_file = os.path.join(args.data_dir, args.train_set, "{}_data.list".format(concat_data_name)) args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "{}_data.list".format(concat_data_name)) if distributed: dist.barrier()
