Batch downloading MP3 files and splitting a text file into individual files.
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😎 can I download MP3 files in batches (I would need 800 approximately) from https://www.text2speech.org/ or a similar site?[github.com][withdata.com][easetext.com]
🤖 Yes, it is possible to download MP3 files in batches from services similar to Text2Speech.org, although the specific functionality may vary depending on the service used. Here are some options:😎 I downloaded this py file from GitHub – mstatt/Text2Speech::
1. **GitHub – mstatt/Text2Speech**: This is a tool that uses Google’s Text 2 Speech API to generate MP3 files from plain text. You need to have a Google Cloud account and enable the Text 2 Speech API. You can then run the `text2speech.py` script, which processes text files placed in a designated directory and outputs them in MP3 format. This approach requires some technical setup but is capable of handling batch processing [ref].
2. **EaseText Text to Speech Converter**: This tool allows for batch conversion of written text into speech from multiple file formats like TXT, DOC, and CSV. This seems to be a user-friendly option for converting a variety of document types into speech files [ref].
3. **TTSBatchConverter**: Offered by Withdata Software, TTSBatchConverter is another tool that enables batch conversion from text files to MP3 audio files. This seems to be an easy and fast solution, requiring only a few mouse clicks. You can download and install TTSBatchConverter for this purpose [ref].
Each of these tools offers a different approach to batch converting text to speech, so you can choose the one that best fits your needs and technical comfort level.[github.com][withdata.com][easetext.com]
import os
import re
import shutil
proc_dir = os.getcwd()+’//articles//’
out_dir = os.getcwd()+’//mp3-out//’
post_proc = os.getcwd()+’//already-processed//’
#Assign file names in the directory to a list for processing
article_list = os.listdir(proc_dir)
#Sort the list
article_list.sort()
##——————————————————————–
def getContents(filename):
with open(os.path.join(proc_dir, filename)) as f:
content_list = f.readlines()
f.close()
contents = ” “.join(content_list)
return contents
##——————————————————————–
def synthesize_text(text, title):
“””Synthesizes speech from the input string of text.”””
from google.cloud import texttospeech
client = texttospeech.TextToSpeechClient()
input_text = texttospeech.SynthesisInput(text=text)
voice = texttospeech.VoiceSelectionParams(
language_code=”en-US”,
name=”en-US-Wavenet-F”,
ssml_gender=texttospeech.SsmlVoiceGender.FEMALE,
)
audio_config = texttospeech.AudioConfig(
audio_encoding=texttospeech.AudioEncoding.MP3
)
response = client.synthesize_speech(
request={“input”: input_text, “voice”: voice, “audio_config”: audio_config}
)
# The response’s audio_content is binary.
with open(os.path.join(out_dir, title+”.mp3″), “wb”) as out:
out.write(response.audio_content)
print(‘Audio content written to file ‘+title+’.mp3″‘)
##——————————————————————–
#Post processing clean up
def clear_out(filename):
shutil.move(os.path.join(proc_dir, filename),os.path.join(post_proc, filename))
for file in article_list:
text =”” #clear out text var
basename = os.path.splitext(file)[0] #extract only file name
print(‘Synthesizing file: ‘+file) # Alert current status
text = getContents(file) #Extract contents of file
synthesize_text(text,basename) #process contents into mp3
clear_out(file) #Move processed file
How do I run this Python code for converting a list of words into speech using my Google Cloud account
🤖 To run the Python code you downloaded from GitHub for converting a list of words into speech using your Google Cloud account, follow these steps:😎 can I call it from Jupyter Lab? I have the text2speech.py script in the same folder as a notebook I’m editing. Can I launch the script from that notebook?
1. **Set Up Your Google Cloud Account**:
– Sign in to your Google Cloud account.
– Set up a project in the Google Cloud Console.
– Enable the Text-to-Speech API for your project.
– Create a service account and download its key file. This key file is a JSON file that allows your Python script to authenticate with the Google Cloud services.
2. **Install the Google Cloud Text-to-Speech Library**:
– Install the library using pip, a package manager for Python. Run `pip install google-cloud-texttospeech` in your terminal or command prompt.
3. **Prepare the Python Script**:
– Place the Python script in a suitable directory on your computer.
– Create folders named `articles`, `mp3-out`, and `already-processed` in the same directory as your script, as the script expects these folders for its input and output.
– Place the text files you want to convert to speech in the `articles` folder. These should be plain text files (.txt).
4. **Set Up Environment Variables**:
– Set the environment variable `GOOGLE_APPLICATION_CREDENTIALS` to the path of the JSON key file you downloaded. This step is crucial for authentication.
– On Windows, you can set this using `set GOOGLE_APPLICATION_CREDENTIALS=[path to your JSON key file]`.
– On macOS/Linux, use `export GOOGLE_APPLICATION_CREDENTIALS=”[path to your JSON key file]”`.
5. **Run the Script**:
– Open a terminal or command prompt.
– Navigate to the directory where your script is located.
– Run the script using Python by typing `python [script_name].py`, replacing `[script_name]` with the name of your Python script.
6. **Check the Output**:
– After running the script, it will process each text file in the `articles` folder and output the corresponding MP3 files in the `mp3-out` folder.
– Processed text files will be moved to the `already-processed` folder.
Ensure that you have Python installed on your computer, and you are comfortable with basic Python operations and using the command line. If you encounter any errors, they are likely related to missing packages, incorrect file paths, or issues with Google Cloud authentication.
🤖 Yes, you can execute the `text2speech.py` script from a Jupyter Lab notebook that’s in the same folder. To do this, you can use the `%run` magic command. Here’s how you can do it:😎 DefaultCredentialsError: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.
1. Open your Jupyter Lab notebook.
2. In a new cell, type `%run text2speech.py`.
3. Run this cell.
This command will execute the `text2speech.py` script as if you were running it directly in a terminal or command prompt. Ensure that all the necessary setup, including the environment variables and folder structure, is properly configured as if you were running the script outside Jupyter Lab.
How can I fix this?
Please don’t tell me the basics of Google Cloud again. Help me fix the problem. I’ve been repeatedly setting up the API key on Google Cloud for hours and nobody seems to be able to tell me what I need to do. Please don’t tell me “you need your API key from Google Cloud”. Try to understand what’s wrong with my Google Cloud setup
🤖 If you’re encountering a `DefaultCredentialsError` even after setting up your API key, the issue might be related to how your environment is recognizing the credentials. Here are some steps to troubleshoot:😎 OK, It’s working now. Now can you help me build a script that splits a text file with approximately 800 rows into one file per row?
1. **Verify the Environment Variable**: Ensure that the `GOOGLE_APPLICATION_CREDENTIALS` environment variable is set correctly in the environment where Jupyter Lab is running. This variable should point to the JSON file containing your service account key.
2. **Set Environment Variable in Jupyter Notebook**: If Jupyter Lab isn’t recognizing the environment variable, set it directly in your notebook:python import os os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/your/service-account-file.json"
Replace `/path/to/your/service-account-file.json` with the actual path to your JSON key file. Do this before running the `%run text2speech.py` command.
3. **Check JSON Key File Path**: Ensure the path to the JSON key file is correct and accessible from the notebook’s runtime environment.
4. **Restart Jupyter Kernel**: After setting the environment variable, restart the Jupyter kernel to ensure the environment is updated.
5. **Check Service Account Permissions**: Make sure the service account linked to the JSON key file has the necessary permissions to access the Text-to-Speech API.
6. **Verify API Enablement**: Double-check that the Text-to-Speech API is enabled for your project on the Google Cloud Console.
By setting the environment variable directly in your Jupyter Notebook and ensuring the path to your JSON key file is correct, you should be able to resolve the credentials issue.
🤖 Certainly! To split a text file with approximately 800 rows into separate files, each containing one row, you can use the following Python script. This script assumes each row in your original file represents the content you want in a separate file:😎 can you do it so the file name includes the row number? “file1”, “file2”, etc.python def split_file_into_individual_files(filename, output_directory): with open(filename, 'r') as file: lines = file.readlines() for i, line in enumerate(lines): output_file = f'{output_directory}/file_{i+1}.txt' with open(output_file, 'w') as outfile: outfile.write(line) # Usage input_file = 'your_input_file.txt' # Replace with your file name output_dir = 'output_files' # Replace with your desired output directory split_file_into_individual_files(input_file, output_dir)
This script reads each line from the input file and writes it to a new file in the specified output directory. Files are named sequentially (`file_1.txt`, `file_2.txt`, etc.). Before running the script, make sure to replace `’your_input_file.txt’` with the name of your input file and `’output_files’` with the name of the directory where you want the split files to be saved. If the output directory does not exist, you will need to create it beforehand or modify the script to create it for you.
🤖 Absolutely! To modify the script so that the output files include the row number in their names (like “file1”, “file2”, etc.), you can slightly adjust the script as follows:python def split_file_into_individual_files(filename, output_directory): with open(filename, 'r') as file: lines = file.readlines() for i, line in enumerate(lines): output_file = f'{output_directory}/file{i+1}.txt' # Adjusted file naming here with open(output_file, 'w') as outfile: outfile.write(line) # Usage input_file = 'your_input_file.txt' # Replace with your file name output_dir = 'output_files' # Replace with your desired output directory split_file_into_individual_files(input_file, output_dir)
This adjustment in the `output_file` line changes the naming format to “file1”, “file2”, etc., reflecting the row number from the original file. Remember to ensure the output directory exists before running this script.
Hugging Face Dataset Metrics
All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: