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from typing import List | |
from pathlib import Path | |
import pandas as pd | |
def get_sentence_data(filename: str, timestamp_dict: dict) -> pd.DataFrame: | |
"""Extracts the sentences from the output dictionary of whisper inference | |
Parameters | |
---------- | |
filename : str | |
Name of the audio analyzed | |
timestamp_dict : dict | |
Output dictionary from whisper inference | |
Returns | |
------- | |
pd.DataFrame | |
DataFrame containing audio filename, start, end and duration of sentences with | |
its transcriptions. | |
""" | |
sentence_df = pd.DataFrame( | |
columns=["Audio file", "Sentence", "Start", "End", "Duration"] | |
) | |
for sentence_i in timestamp_dict["segments"]: | |
sentence_i = pd.DataFrame( | |
{ | |
"Audio file": [filename], | |
"Sentence": [str(sentence_i["text"])], | |
"Start": [sentence_i["start"]], | |
"End": [sentence_i["end"]], | |
"Duration": [sentence_i["end"] - sentence_i["start"]], | |
} | |
) | |
sentence_df = pd.concat([sentence_df, sentence_i], ignore_index=True) | |
return sentence_df | |
def get_word_data(filename: str, timestamp_dict: dict): | |
"""Extracts the words from the output dictionary of whisper inference | |
Parameters | |
---------- | |
filename : str | |
Name of the audio analyzed | |
timestamp_dict : dict | |
Output dictionary from whisper inference | |
Returns | |
------- | |
pd.DataFrame | |
DataFrame containing audio filename, start, end and duration of words with | |
its transcriptions. | |
""" | |
word_df = pd.DataFrame(columns=["Audio file", "Word", "Start", "End", "Duration"]) | |
for sentence_i in timestamp_dict["segments"]: | |
for word_i in sentence_i["words"]: | |
word_i_df = pd.DataFrame( | |
{ | |
"Audio file": [filename], | |
"Word": [str(word_i["text"])], | |
"Start": [word_i["start"]], | |
"End": [word_i["end"]], | |
"Duration": [word_i["end"] - word_i["start"]], | |
} | |
) | |
word_df = pd.concat([word_df, word_i_df], ignore_index=True) | |
return word_df | |
def filter_dataframe_by_audiofile(timestamps_df: pd.DataFrame, audio_file: str) -> List: | |
"""Generates a list from timestamps_df with the timestamps belonging to audio_file | |
Parameters | |
---------- | |
timestamps_df : pd.DataFrame | |
Dataframe containing timestamps | |
audio_file : str | |
Name of the audio file. | |
Returns | |
------- | |
List | |
List of tuples containing the start and end of each stamp. | |
E.g: [(start_1, end_2), ..., (start_n, end_n)] | |
""" | |
audio_df = timestamps_df[timestamps_df["Audio file"] == audio_file] | |
return list(zip(audio_df["Start"], audio_df["End"])) | |
def get_utterances_transcriptions(timestamps_df: pd.DataFrame) -> List[str]: | |
"""Gives column with transcriptions | |
Parameters | |
---------- | |
timestamps_df : pd.DataFrame | |
DataFrame with transcriptions | |
Returns | |
------- | |
List[str] | |
List of the transcriptions | |
""" | |
return timestamps_df.iloc[:, 1].tolist() | |
def save_transcriptions_segments( | |
audio_path: Path, transcriptions_list: List[str], destination: Path | |
) -> None: | |
"""Save transcription segments to text files. | |
Parameters | |
---------- | |
audio_path : Path | |
Path to the audio file. | |
transcriptions_list : List[str] | |
List of transcriptions. | |
destination : Path | |
Destination path for the text files. | |
""" | |
for i, transcription_i in enumerate(transcriptions_list): | |
transcription_i_path = destination / f"{audio_path.stem}-{i}.txt" | |
with open(str(transcription_i_path), "w") as file: | |
file.write(transcription_i) | |
def generate_transcriptions_splits( | |
audio_path: Path, timestamps_df: pd.DataFrame, destination: Path | |
): | |
"""Generate and save transcription splits based on timestamps. | |
Parameters | |
---------- | |
audio_path : Path | |
Path to the audio file. | |
timestamps_df : pd.DataFrame | |
DataFrame containing timestamps. | |
destination : Path | |
Destination path for the text files. | |
""" | |
transcriptions_list = get_utterances_transcriptions(timestamps_df) | |
save_transcriptions_segments(audio_path, transcriptions_list, destination) | |