updated txt to mp3 pyton code , for female voice.

 error :

showing, _InactiveRpcError: <_InactiveRpcError of RPC that terminated with:

status = StatusCode.INVALID_ARGUMENT

details = "Requested male voice, but voice en-IN-Neural2-A is a female voice."

debug_error_string = "UNKNOWN:Error received from peer ipv4:74.125.20.95:443 {created_time:"2024-09-18T14:13:09.869505147+00:00", grpc_status:3, grpc_message:"Requested male voice, but voice en-IN-Neural2-A is a female voice."}"

>

The above exception was the direct cause of the following exception:

InvalidArgument Traceback (most recent call last)

/usr/local/lib/python3.10/dist-packages/google/api_core/grpc_helpers.py in error_remapped_callable(*args, **kwargs)

76 return callable_(*args, **kwargs)

77 except grpc.RpcError as exc:

---> 78 raise exceptions.from_grpc_error(exc) from exc

79

80 return error_remapped_callable

InvalidArgument: 400 Requested male voice, but voice en-IN-Neural2-A is a female voice.

--

replaced with 

ssml_gender=texttospeech.SsmlVoiceGender.MALE

ssml_gender=texttospeech.SsmlVoiceGender.FEMALE

import gc

gc.collect()

!pip install google-cloud-texttospeech pydub

import json

from google.cloud import texttospeech

from pydub import AudioSegment

import os

# Paths to the files

credentials_path = '/content/credentials.json'

text_file_path = 'Cloud_తైతాకార రహస్యము త్రైతాకార బెర్ముడా_English_updated_18thsep24.txt'

output_file_path = 'Cloud_తైతాకార రహస్యము త్రైతాకార బెర్ముడా_English_updated_18thsep24_English2_audio.mp3'

# Verify the file existence

if os.path.exists(credentials_path):

    print("Credentials file found.")

else:

    print("Credentials file not found.")

if os.path.exists(text_file_path):

    print("Text file found.")

else:

    print("Text file not found.")

# Load your Google Cloud project credentials

with open(credentials_path) as f:

    credentials = json.load(f)

# Configure the Text-to-Speech client

client = texttospeech.TextToSpeechClient.from_service_account_json(credentials_path)

# Load Telugu text from the file

with open(text_file_path, 'r', encoding='utf-8') as file:

    text = file.read().strip()

# Function to split text into chunks based on byte size

def split_text(text, max_bytes=5000):

    """Split text into chunks of max_bytes bytes."""

    chunks = []

    current_chunk = ""

    current_chunk_bytes = 0

    for char in text:

        char_bytes = len(char.encode('utf-8'))

        if current_chunk_bytes + char_bytes > max_bytes:

            chunks.append(current_chunk)

            current_chunk = char

            current_chunk_bytes = char_bytes

        else:

            current_chunk += char

            current_chunk_bytes += char_bytes

    if current_chunk:

        chunks.append(current_chunk)

    return chunks

# Configure voice and speed

voice = texttospeech.VoiceSelectionParams(

    language_code='en-IN',  # code : te-IN ,Telugu (India) Link:https://cloud.google.com/text-to-speech/docs/voices

    name='en-IN-Neural2-A',  # Male voice te-IN-Standard-B  ;felmale : te-IN-Standard-A;

    #en-IN-Standard-A  female   ,code: en-IN

    #hi-IN-Standard-A FEMALE  hindi  , hindi code: hi-IN

    #ssml_gender=texttospeech.SsmlVoiceGender.MALE

    ssml_gender=texttospeech.SsmlVoiceGender.FEMALE

)

audio_config = texttospeech.AudioConfig(

    audio_encoding=texttospeech.AudioEncoding.MP3,

    speaking_rate=1  # Normal speed, 1.0 is default 0.25,0.5,0.75,1 (normal), 1.25, 1.5 , 1.75, 2

)

# Function to synthesize speech for each chunk

def synthesize_speech(text_chunk):

    synthesis_input = texttospeech.SynthesisInput(text=text_chunk)

    response = client.synthesize_speech(

        input=synthesis_input, voice=voice, audio_config=audio_config

    )

    return response.audio_content

# Function to save audio content to a temporary file

def save_temp_audio(audio_content, temp_file_path):

    with open(temp_file_path, 'wb') as out:

        out.write(audio_content)

# Process text and combine audio files

text_chunks = split_text(text)

temp_files = []

for i, chunk in enumerate(text_chunks):

    temp_file_path = f'/content/temp_chunk_{i}.mp3'

    temp_files.append(temp_file_path)

    audio_content = synthesize_speech(chunk)

    save_temp_audio(audio_content, temp_file_path)

# Combine audio files

combined = AudioSegment.empty()

for temp_file in temp_files:

    audio_segment = AudioSegment.from_mp3(temp_file)

    combined += audio_segment

# Export the combined audio to the final output file

combined.export(output_file_path, format='mp3')

print(f'Audio content written to file "{output_file_path}"')

# Clean up temporary files

for temp_file in temp_files:

    os.remove(temp_file)

---