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Piper Documentation

select_speaker.py

Code Explained

The provided main function is a Python script designed to filter and process speaker-specific data from a CSV input. It allows users to either select a speaker by name or by their rank based on the number of utterances. The script reads input from sys.stdin and writes the filtered output to sys.stdout, making it suitable for use in pipelines. Below is a detailed explanation of its functionality:

Argument Parsing

The script begins by defining two command-line arguments using argparse.ArgumentParser:

  1. --speaker-number: An integer specifying the rank of the speaker to select based on the number of utterances.
  2. --speaker-name: A string specifying the name of the speaker to filter.

The script ensures that at least one of these arguments is provided using an assert statement. This guarantees that the user specifies a valid filtering criterion.

CSV Reader and Writer

The script uses Python’s csv module to handle input and output:

  • A csv.reader is created to read rows from sys.stdin, with | as the delimiter.
  • A csv.writer is initialized to write rows to sys.stdout, also using | as the delimiter.

This setup allows the script to process CSV data in a streaming fashion, making it efficient for large datasets.

Filtering by Speaker Name

If the --speaker-name argument is provided, the script iterates through each row in the input CSV. For each row:

  1. It extracts the audio file path, speaker_id, and text fields.
  2. If the speaker_id matches the specified --speaker-name, the script writes the audio and text fields to the output using the csv.writer.

This mode is straightforward and directly filters rows based on the speaker’s name.

Filtering by Speaker Number

If the --speaker-number argument is provided, the script performs the following steps:

  1. Group Utterances by Speaker: It uses a defaultdict to group rows by speaker_id, storing each row’s audio and text fields.
  2. Count Utterances per Speaker: A Counter is used to count the number of utterances for each speaker_id.
  3. Rank Speakers: The most_common method of the Counter is used to rank speakers by the number of utterances in descending order.
  4. Select the Target Speaker: The script iterates through the ranked speakers using enumerate. When the index matches the specified --speaker-number, it writes all rows for that speaker to the output and prints the speaker_id to sys.stderr.

This mode is useful for selecting the most active speakers or analyzing data for a specific rank.

Key Features

  1. Flexible Filtering: Supports filtering by either speaker name or rank, catering to different use cases.
  2. Streaming Processing: Reads from sys.stdin and writes to sys.stdout, enabling integration with other tools in a data pipeline.
  3. Efficient Grouping and Counting: Uses defaultdict and Counter for efficient data aggregation and ranking.
  4. Error Handling: Ensures that at least one filtering criterion is provided, preventing invalid usage.

Use Case

This script is ideal for preprocessing or analyzing speaker-specific data in datasets where utterances are associated with speakers. It can be used in text-to-speech (TTS) pipelines, speaker recognition tasks, or any scenario requiring speaker-based filtering of audio-text pairs. Its ability to handle large datasets in a streaming manner makes it highly scalable and efficient.

Source Code

#!/usr/bin/env python3
import argparse
import csv
import sys
from collections import Counter, defaultdict




def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--speaker-number", type=int)
    parser.add_argument("--speaker-name")
    args = parser.parse_args()


    assert (args.speaker_number is not None) or (args.speaker_name is not None)


    reader = csv.reader(sys.stdin, delimiter="|")
    writer = csv.writer(sys.stdout, delimiter="|")


    if args.speaker_name is not None:
        for row in reader:
            audio, speaker_id, text = row[0], row[1], row[-1]
            if args.speaker_name == speaker_id:
                writer.writerow((audio, text))
    else:
        utterances = defaultdict(list)
        counts = Counter()
        for row in reader:
            audio, speaker_id, text = row[0], row[1], row[-1]
            utterances[speaker_id].append((audio, text))
            counts[speaker_id] += 1


        writer = csv.writer(sys.stdout, delimiter="|")
        for i, (speaker_id, _count) in enumerate(counts.most_common()):
            if i == args.speaker_number:
                for row in utterances[speaker_id]:
                    writer.writerow(row)


                print(speaker_id, file=sys.stderr)
                break




if __name__ == "__main__":
    main()