def write_manifest(frame, name): length = frame.shape[0] for pos in range(length): print(pos, " von ", length) manifest["id"].append(frame.values[pos][0]) manifest["audio"].append(frame.values[pos][1]) manifest["n_frames"].append(frame.values[pos][2]) manifest["tgt_text"].append(frame.values[pos][3]) df_manifest = pd.DataFrame.from_dict(manifest) save_df_to_tsv(df_manifest, Path(rootpath) / f"{name}.tsv")
def generate_manifest(split, manifest): df = pd.DataFrame.from_dict(manifest) save_df_to_tsv(df, Path(root_path_data) / f"{split}_{task}.tsv")
counter_test = 0 for audio in os.listdir(path): path_id = path + "/" + audio id = audio.replace(dialect + "_", "").replace(".wav", "") if counter % 2 == 0 and counter_test < split_size: audio_processing(path_id, folder, audio, int(id), test) counter_test = counter_test + 1 if counter % 3 == 0 and counter_dev < split_size: audio_processing(path_id, folder, audio, int(id), dev) counter_dev = counter_dev + 1 else: audio_processing(path_id, folder, audio, int(id), train) counter = counter + 1 df = pd.DataFrame.from_dict(train) save_df_to_tsv(df, Path(root) / f"train_st_ch_de.tsv") df = pd.DataFrame.from_dict(test) save_df_to_tsv(df, Path(root) / f"test_st_ch_de.tsv") df = pd.DataFrame.from_dict(dev) save_df_to_tsv(df, Path(root) / f"dev_st_ch_de.tsv") spm_filename_prefix = f"spm_char_st_ch_de" # Generate config YAML gen_config_yaml( Path(root), spm_filename_prefix + ".model", yaml_filename=f"config_st_ch_de.yaml", specaugment_policy="lb", ) # generating vocabulary if len(train_text) > 0:
def generate_manifest(manifest, path): df = pd.DataFrame.from_dict(manifest) save_df_to_tsv(df, Path(path))
import pandas as pd from data_utils import save_df_to_tsv from pathlib import Path MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text"] manifest = {c: [] for c in MANIFEST_COLUMNS} rootpath = "/Users/bogumiladubel/Documents/BA/data/st/eth_swiss_dialects/split_dialect/swissdial/" frame = pd.read_csv(rootpath + "test_diff.csv") length = frame.shape[0] for pos in range(length): print(pos, " von ", length) manifest["id"].append(frame.values[pos][1]) manifest["audio"].append(frame.values[pos][2]) manifest["n_frames"].append(frame.values[pos][3]) manifest["tgt_text"].append(frame.values[pos][4]) df_manifest = pd.DataFrame.from_dict(manifest) name = "test_diff_zh" save_df_to_tsv(df_manifest, Path(rootpath) / f"{name}.tsv")
def save_manifest(file, manifest): df = pd.DataFrame.from_dict(manifest) save_df_to_tsv(df, Path(root_path_data) / f"{file}.tsv")
return transcript.strip() for t in range(df.shape[0]): if t % 1000 == 0: print(t, " von ", df.shape[0]) manifest["id"].append(df.values[t][0]) manifest["audio"].append(df.values[t][1]) manifest["n_frames"].append(df.values[t][2]) target = preprocess_transcript(str(df.values[t][3])) train_text.append(target) manifest["tgt_text"].append(target) manifest["speaker"].append(df.values[t][4]) df = pd.DataFrame.from_dict(manifest) save_df_to_tsv(df, Path(rootpath) / f"{file}") def gen_voc(train_text, spm_filename_prefix): f = open(Path(root_path_data) / "test.txt", "a") for t in train_text: f.write(" ".join(t) + "\n") print(f.name) gen_vocab(Path(f.name), Path(root_path_data) / spm_filename_prefix) task = "asr_de" spm_filename_prefix = f"spm_char_{task}" # Generate config YAML gen_config_yaml( Path(root_path_data),