Forum - Socoder

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Socoder -> AI Threads -> Machine Made Max
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Sun, 05 Mar 2023, 03:06
spinal	Previously in the ChatGPT thread... Well there goes my chance to have 'someone' code a max headroom for me ðŸ˜Ÿ -=-=-=- Well, it can not do the following... 1. draw a head that looks like max headroom. It;s just a robot head made out of rectangles. 2. can not animate the head, once you try it no longer draws anything. -=-=- Check out my excellent homepage!
Sun, 05 Mar 2023, 04:12
spinal	I can see me wasting my whole day playing with this, it's quite addictive. -=-=- Check out my excellent homepage!
Sun, 05 Mar 2023, 04:41
spinal	So far, it's not getting better at my original request, just going around in circles I think. Doesn't help that I don't know python either. -=-=- Check out my excellent homepage!
Sun, 05 Mar 2023, 04:59
Jayenkai	Are you talking about the Chat or the Art AI? I'm assuming the art, at this point, because otherwise getting the chat to draw things is insane!! Is it trying to code a display routine the face? That'd be neat. -=-=- ''Load, Next List!''
Sun, 05 Mar 2023, 05:17
spinal	yeah, its trying to code a face, it looks like a robot. when I asked it to animate it, it just moved the outline diagonally off the screen -=-=- Check out my excellent homepage!
Sun, 05 Mar 2023, 05:29
Jayenkai	Good grief!! Well then.. um.. Good luck with that!! -=-=- ''Load, Next List!''
Sun, 05 Mar 2023, 09:17
spinal	I actually think this is going quite well, considering I don't know python, and technically it doesn't either. All it really knows is what words are motl likely to go next to each other. I imagine on a deep down level, thats roughly how our brains work, only bilions of times bigger. I'm working on the stuttering text for now, its too fun -=-=- Check out my excellent homepage!
Sun, 05 Mar 2023, 10:00
spinal	socoder.net/uploads/124/2023/09/output.wav Getting there. import pyttsx3 import random import time import numpy as np import wave import playsound # Initialize the TTS engine with the default voice engine = pyttsx3.init() # Get the user input text = input("Enter the text to speak: ") # Define a variable to hold the audio rate audio_rate = 0 # Define a function to speak the sentence and return the audio data, rate, and format def speak(sentence): # Speak the sentence and save it to a WAV file engine.save_to_file(sentence, 'temp.wav') engine.runAndWait() # Read the audio data from the saved WAV file with wave.open('temp.wav', 'rb') as wav_file: audio_data = wav_file.readframes(wav_file.getnframes()) # Convert the audio data to a NumPy array audio_data = np.frombuffer(audio_data, dtype=np.int16) # Find the start and end indices of the non-silent audio data start_index = np.nonzero(audio_data)[0][0] end_index = np.nonzero(audio_data)[0][-1] + 1 # Trim the silence from the beginning and end of the audio data audio_data = audio_data[start_index:end_index] # Get the audio format and rate audio_format = wav_file.getsampwidth() audio_rate = wav_file.getframerate() return audio_data, audio_rate, audio_format # Define a function to add a stutter effect to each word def stutter(word): # Speak the word and get the audio data, rate, and format audio_data, audio_rate_word, audio_format_word = speak(word) # Check if the word is short if len(word) <= 3: # Stutter the whole word repeated_audio = np.concatenate([audio_data] * 3) else: # Choose a random position to stutter from position = random.uniform(0.0, 0.25) # Get the duration of the word's audio duration = len(audio_data) / audio_rate_word # Calculate the start and end times of the stuttered audio start_time = duration * position end_time = duration * (position + 0.25) # Calculate the start and end samples of the stuttered audio start_sample = int(start_time * audio_rate_word) end_sample = int(end_time * audio_rate_word) # Extract the stuttered audio and repeat it stuttered_audio = audio_data[start_sample:end_sample] repeated_audio = np.concatenate([stuttered_audio] * 3) repeated_audio = repeated_audio.astype(np.int16) # Concatenate the rest of the word to the repeated audio rest_of_word = audio_data[end_sample:] repeated_audio = np.concatenate([repeated_audio, rest_of_word]) return repeated_audio # Speak the text with random stuttering and save it to a WAV file audio_data = np.array([], dtype=np.int16) sentence = ' '.join(text.split()) for i, word in enumerate(sentence.split()): # Speak the word speak(word) # Decide whether to stutter this word should_stutter = random.choice([True, False, False, False, False]) if should_stutter: stuttered_audio = stutter(word) audio_data = np.concatenate([audio_data, stuttered_audio]) else: # Get the audio data, rate, and format for the spoken word word_audio_data, audio_rate_word, audio_format_word = speak(word) # Resample the audio data if necessary if audio_rate != 0 and audio_rate_word != audio_rate: audio_data_word_resampled = np.int16(np.round(np.interp(np.linspace(0, len(word_audio_data), int(len(word_audio_data) * audio_rate / audio_rate_word)), np.linspace(0, len(word_audio_data), len(word_audio_data)), word_audio_data))) else: audio_data_word_resampled = word_audio_data audio_data = np.concatenate([audio_data, audio_data_word_resampled]) # Update the audio rate audio_rate = audio_rate_word # Add a delay between words if i < len(sentence.split()) - 1: time.sleep(0.05) # Wait for the sentence to finish engine.runAndWait() # Write the audio data to a WAV file with wave.open('output.wav', 'wb') as wav_file: wav_file.setnchannels(1) wav_file.setsampwidth(audio_format_word) wav_file.setframerate(audio_rate) wav_file.writeframes(audio_data.tobytes()) # Play the resulting audio playsound.playsound('output.wav') --v I hit a character limit, so told it ot shorten the variable names, now its a bit harder to read, but it fits in the window -=-=- Check out my excellent homepage!
Sun, 05 Mar 2023, 10:02
Jayenkai	LOL!!
Sun, 05 Mar 2023, 10:15
spinal	Having a hell of a job getting it to remove the gaps between the words. -=-=- Check out my excellent homepage!
Sun, 05 Mar 2023, 10:44
Pakz	That looks amazing. I might try and do a bit of python. There is a continue button to lengthen the code. Typing continue lengthens code too. Not sure what you ment.
Sun, 05 Mar 2023, 10:44
Jayenkai	How much control do you have? Is there a "in text" parameter to control the speed? -=-=- ''Load, Next List!''
Sun, 05 Mar 2023, 11:04
spinal	Don't know, I have no idea what it's doing import pyttsx3 import random import time import numpy as np import wave import playsound # Initialize the TTS engine with the default voice engine = pyttsx3.init() # Get the user input text = input("Enter the text to speak: ") # Define a variable to hold the audio rate audio_rate = 0 # Define a function to speak the sentence and return the audio data, rate, and format def speak(sentence, silence_threshold=5, amplitude_threshold=10, reduction_factor=100, max_sentence_length=20): # Split the sentence into smaller parts if it's too long if len(sentence) > max_sentence_length: sentence_parts = [sentence[i:i+max_sentence_length] for i in range(0, len(sentence), max_sentence_length)] else: sentence_parts = [sentence] # Process each sentence part and concatenate the audio data audio_data_list = [] for sentence_part in sentence_parts: # Speak the sentence and get the audio data, rate, and format engine.save_to_file(sentence_part, 'temp.wav') engine.runAndWait() with wave.open('temp.wav', 'rb') as wav_file: audio_data = wav_file.readframes(wav_file.getnframes()) audio_data_list.append(audio_data) audio_data = b''.join(audio_data_list) # Convert the audio data to a NumPy array audio_data = np.frombuffer(audio_data, dtype=np.int16) # Find the start and end indices of the non-silent audio data start_index = np.nonzero(audio_data > amplitude_threshold)[0][0] end_index = np.nonzero(audio_data > amplitude_threshold)[0][-1] + 1 # Remove the silent audio data between the start and end indices audio_data = audio_data[start_index:end_index] # Calculate the durations of the silence between the words silence_durations = np.diff(np.nonzero(np.concatenate(([0], np.diff(np.nonzero(audio_data > amplitude_threshold)[0]), [1])) == 1)[0]) # Find the indices of the silence durations that are shorter than the threshold short_silence_indices = np.nonzero(silence_durations < (silence_threshold * wav_file.getframerate() / 1000))[0] # Reduce the amplitude of the short silences to a very low value for i in short_silence_indices: start_index = end_index + int(silence_durations[i] * wav_file.getframerate() / 1000) next_non_silent_index = np.nonzero(audio_data[start_index:] > amplitude_threshold)[0] if len(next_non_silent_index) > 0: end_index = next_non_silent_index[0] + start_index audio_data[start_index:end_index] //= reduction_factor else: break # Get the audio format and rate audio_format = wav_file.getsampwidth() audio_rate = wav_file.getframerate() return audio_data, audio_rate, audio_format # Define a function to add a stutter effect to each word def stutter(word): # Speak the word and get the audio data, rate, and format audio_data, audio_rate_word, audio_format_word = speak(word) # Check if the word is short if len(word) <= 3: # Stutter the whole word repeated_audio = np.concatenate([audio_data] * 3) else: # Choose a random position to stutter from position = random.uniform(0.0, 0.25) # Get the duration of the word's audio duration = len(audio_data) / audio_rate_word # Calculate the start and end times of the stuttered audio start_time = duration * position end_time = duration * (position + 0.25) # Calculate the start and end samples of the stuttered audio start_sample = int(start_time * audio_rate_word) end_sample = int(end_time * audio_rate_word) # Extract the stuttered audio and repeat it stuttered_audio = audio_data[start_sample:end_sample] repeated_audio = np.concatenate([stuttered_audio] * 3) repeated_audio = repeated_audio.astype(np.int16) # Concatenate the rest of the word to the repeated audio rest_of_word = audio_data[end_sample:] repeated_audio = np.concatenate([repeated_audio, rest_of_word]) return repeated_audio # Speak the text with random stuttering and save it to a WAV file audio_data = np.array([], dtype=np.int16) sentence = ' '.join(text.split()) for i, word in enumerate(sentence.split()): # Speak the word speak(word) # Decide whether to stutter this word should_stutter = random.choice([True, False, False, False, False]) if should_stutter: stuttered_audio = stutter(word) audio_data = np.concatenate([audio_data, stuttered_audio]) else: # Get the audio data, rate, and format for the spoken word word_audio_data, audio_rate_word, audio_format_word = speak(word) # Resample the audio data if necessary if audio_rate != 0 and audio_rate_word != audio_rate: audio_data_word_resampled = np.int16(np.round(np.interp(np.linspace(0, len(word_audio_data), int(len(word_audio_data) * audio_rate / audio_rate_word)), np.linspace(0, len(word_audio_data), len(word_audio_data)), word_audio_data))) else: audio_data_word_resampled = word_audio_data audio_data = np.concatenate([audio_data, audio_data_word_resampled]) # Update the audio rate audio_rate = audio_rate_word # Add a delay between words if i < len(sentence.split()) - 1: time.sleep(0.05) # Wait for the sentence to finish engine.runAndWait() # Write the audio data to a WAV file with wave.open('output.wav', 'wb') as wav_file: wav_file.setnchannels(1) wav_file.setsampwidth(audio_format_word) wav_file.setframerate(audio_rate) wav_file.writeframes(audio_data.tobytes()) # Play the resulting audio playsound.playsound('output.wav') --v -=-=- Check out my excellent homepage!
Sun, 05 Mar 2023, 13:02
spinal	I don't seem to be able to get any further with this, it doesn't seem to be able to keep track of whats going on at this point. Not bad for a couple of hours time wasting though. -=-=- Check out my excellent homepage!
Sun, 05 Mar 2023, 13:34
Jayenkai	I had a think in the bath.. You're currently doing each word on its own, which, I think, is the cause of the gaps. Can you, at random intervals, Stack the next couple of words together.. include spaces, still, but .. I would expect the speech engine to do that without as much of a gap. You currently do For each Word Speak(Word) If rnd(), add stutter Next --v Instead do For each Word If rnd() Word=Word+" "+NextWord Speak(Word) If rnd(), add stutter Next --v (Or thereabouts.) I think it might help cluster the words together a bit more.. um.. Max-like! -=-=- ''Load, Next List!''
Sun, 05 Mar 2023, 13:58
spinal	I was thinking that, but the chat kept breaking the code. I think I asked too much of it in one topic. It's supposed to split up the input text into smaller chunks as it was taking a while to parse and output, but I think it misunderstood, it seems to have split it up, but output it all in one go regardless, so it still takes a while. -=-=- Check out my excellent homepage!
Tue, 07 Mar 2023, 04:06
spinal	I think I might do some googling to find a different TTS and language combination... -=-=- Check out my excellent homepage!
Tue, 07 Mar 2023, 04:54
Jayenkai	If you don't mind ChatGPT losing its memory, consider hitting the "Wipe Clean" button and asking it again from scratch. It very rarely comes up with the same solution for the same problem. -=-=- ''Load, Next List!''
Tue, 07 Mar 2023, 05:29
spinal	To be fair, it seems to occasionally lose its memory anyway. -=-=- Check out my excellent homepage!
Tue, 07 Mar 2023, 05:55
Jayenkai	Yeah, it can't always keep track of the entire conversation, but clearing it out will give it a whole new perspective!!! This is why I wrote that GotoJSE example thing, earlier. So I can say in one go "Here's the language, now start doing shit!!" but with a fresh start each time. Makes for some interesting differences. -=-=- ''Load, Next List!''
Wed, 08 Mar 2023, 03:26
spinal	import random import time import wave import tempfile import os import numpy as np import pyttsx3 import pyaudio def speak(text): # Initialize the text-to-speech engine engine = pyttsx3.init() # Set the text-to-speech parameters engine.setProperty('rate', 200) engine.setProperty('volume', 1.0) voices = engine.getProperty('voices') engine.setProperty('voice', voices[2].id) # Generate a temporary WAV file to save the audio data with tempfile.NamedTemporaryFile(delete=False) as f: file_path = f.name # Speak the text and save the audio data to the temporary WAV file engine.save_to_file(text, file_path) engine.runAndWait() # Read the audio data from the temporary WAV file with wave.open(file_path, 'rb') as wav_file: audio_data = np.frombuffer(wav_file.readframes(-1), dtype=np.int16) audio_rate = wav_file.getframerate() audio_format = wav_file.getsampwidth() # Remove the temporary WAV file os.remove(file_path) return audio_data, audio_rate, audio_format def stutter(word): # Speak the word and get the audio data, rate, and format audio_data, audio_rate, audio_format = speak(word) # Convert the audio data to a numpy array audio_data = np.frombuffer(audio_data, dtype=np.int16) # Check if the word is short if len(word) <= 3: # Stutter the whole word repeated_audio = np.concatenate([audio_data] * 3) else: # Choose a random position to stutter from position = random.uniform(0.0, 0.5) # Get the duration of the word's audio duration = len(audio_data) / audio_rate # Calculate the start and end times of the stuttered audio start_time = duration * position end_time = duration * (position + 0.25) # Calculate the start and end frames of the stuttered audio start_frame = int(start_time * audio_rate) end_frame = int(end_time * audio_rate) # Extract the stuttered audio and repeat it stuttered_audio = audio_data[start_frame:end_frame] repeated_audio = np.concatenate([stuttered_audio] * 3) repeated_audio = repeated_audio.astype(np.int16) return repeated_audio, audio_rate, audio_format def stutter_text_from_file(file_path): # Read the input text from the file with open(file_path, 'r') as f: text = f.read() # Split the text into words words = text.split() num_words = len(words) # Check if there are any words in the input text if num_words == 0: print('Error: No words found in input text') return word_count = 0 audio_data_list = [] while word_count <= num_words: # Choose a random number of words to speak normally num_normal_words = random.randint(4, 10) if num_normal_words > num_words - word_count: num_normal_words = num_words - word_count # Speak the first few words normally normal_text = ' '.join(words[word_count:word_count + num_normal_words]) word_count += num_normal_words normal_audio_data, audio_rate, audio_format = speak(normal_text) # Check if there are any words left to stutter if word_count >= num_words: audio_data_list.append(normal_audio_data) break # Stutter the next word stuttered_word = words[word_count] stuttered_audio_data, audio_rate, audio_format = stutter(stuttered_word) # Repeat the stuttered word repeat_audio_data, audio_rate, audio_format = speak(stuttered_word) # Concatenate the audio data and append it to the list audio_data_list.append(normal_audio_data) audio_data_list.append(stuttered_audio_data) audio_data_list.append(repeat_audio_data) # Increment the word count word_count += 1 # Concatenate all the audio data and write it to a stream audio_data = np.concatenate(audio_data_list) player = pyaudio.PyAudio() stream = player.open(format=player.get_format_from_width(audio_format), channels=1, rate=audio_rate, output=True) stream.write(audio_data.tobytes()) stream.stop_stream() stream.close() player.terminate() if __name__ == '__main__': # Get the path to the input file file_path = 'readme.txt' # Stutter the text from the file stutter_text_from_file(file_path) --v It took a slightly different approach to this one. It's more on the right track. Also it reads from readme.txt and attempts to change the voice. -=-=- Check out my excellent homepage!
Wed, 08 Mar 2023, 04:11
Jayenkai	Oh, I really wish I could get that to run.. > Reveal 🔎 Stupid Python. I have 3 installs of Python on the Mac, btw, from all the Stable Diffusion trials I've been doing. It eventually fails at the voice bit, suggesting that "attr['VoiceAge']" is an unknown attribute, but that's nowhere in your code, so god only knows where it's picking that up from. Meanwhile, on Win11Arm in a VM on the Mac. Installed Python, tried to run, asked for numpy. Tried to install numpy, asked for VisStudio C++ build tools. Installed VisStudio C++ build tools. Tried to install numpy, asked for VisStudio C++ build tools. Still asked for c++ build tools Restarted Windows Tried to install numpy, asked for VisStudio C++ build tools. Don't think that's going to work, either. Is there a possibility that it could be compiled?! -=-=- ''Load, Next List!''
Wed, 08 Mar 2023, 05:02
spinal	Not a clue, I'll look into it tomorrow ðŸ‘ -=-=- Check out my excellent homepage!
Fri, 10 Mar 2023, 03:21
spinal	It's getting better... import random import time import wave import tempfile import os import numpy as np import pyttsx3 import pyaudio import re #from datetime import datetime #random.seed(datetime.now().timestamp()) def speak(text): # Initialize the text-to-speech engine engine = pyttsx3.init() # Set the text-to-speech parameters engine.setProperty('rate', 200) engine.setProperty('volume', 1.0) engine.setProperty('pitch', random.uniform(0.9, 1.1)) # Set pitch to random value # Generate a temporary WAV file to save the audio data with tempfile.NamedTemporaryFile(delete=False) as f: file_path = f.name # Speak the text and save the audio data to the temporary WAV file engine.save_to_file(text, file_path) # Start the speech engine in a separate thread engine.startLoop(False) # Wait for the engine to finish speaking while engine.iterate(): time.sleep(0.1) Print('Working...') # Stop the speech engine engine.stop() # Read the audio data from the temporary WAV file with wave.open(file_path, 'rb') as wav_file: audio_data = np.frombuffer(wav_file.readframes(-1), dtype=np.int16) audio_rate = wav_file.getframerate() audio_format = wav_file.getsampwidth() start_trim = 0 duration = len(audio_data)-1 end_trim = duration threshold = 10 while audio_data[start_trim] < threshold and start_trim < duration : start_trim = start_trim + 1 while audio_data[end_trim] < threshold and end_trim > 0 : end_trim = end_trim - 1 #string = str(start_trim)+','+str(duration)+','+str(end_trim) #print(string) # Remove the temporary WAV file os.remove(file_path) return audio_data[start_trim:end_trim], audio_rate, audio_format def stutter(word): # Speak the word and get the audio data, rate, and format audio_data, audio_rate, audio_format = speak(word) # Convert the audio data to a numpy array audio_data = np.frombuffer(audio_data, dtype=np.int16) # Choose a random position to stutter from #position = random.uniform(0.0, 0.5) # Get the duration of the word's audio duration = len(audio_data) / audio_rate # Calculate the start and end times of the stuttered audio start_time = 0 end_time = duration * random.uniform(0.4, 0.6) if len(word) <= 3: start_time = 0 end_time = duration - 0.6 # Calculate the start and end frames of the stuttered audio start_frame = int(start_time * audio_rate) end_frame = int(end_time * audio_rate) # Extract the stuttered audio and repeat it stuttered_audio = audio_data[start_frame:end_frame] repeated_audio = np.concatenate([stuttered_audio] * 2) #repeated_audio = repeated_audio.astype(np.int16) return repeated_audio, audio_rate, audio_format def stutter_text(text): # Split the text into sentences sentences = re.split('[.?!\n]', text) #print(text) # put a word after every text, to make the tts sound like it's continueing a sentence, then remove that word from the audio normal_audio_data, audio_rate, audio_format = speak("test") test_size = int(len(normal_audio_data)0.85) # Iterate over each sentence and stutter some of the words audio_data_list = [] for sentence in sentences: sentence = sentence + '.' print(sentence) # Split the sentence into words words = sentence.strip().split() # Check if there are any words in the sentence if len(words) == 0: continue # Process each word in the sentence i = 0 while i < len(words): # Choose a random number of words to speak normally num_normal_words = random.randint(5, 10) if i + num_normal_words > len(words): num_normal_words = len(words) - i # Speak the first few words normally normal_text = ' '.join(words[i:i+num_normal_words]) normal_text = normal_text + ' test ' # the word 'test' is removed from the audio afterwards. normal_audio_data, audio_rate, audio_format = speak(normal_text) duration = len(normal_audio_data)-test_size audio_data_list.append(normal_audio_data[0:duration]) # Check if there are any words left to stutter if i + num_normal_words >= len(words): break # Stutter the next word stuttered_word = words[i+num_normal_words] stuttered_audio_data, audio_rate, audio_format = stutter(stuttered_word) # Repeat the stuttered word repeat_audio_data, audio_rate, audio_format = speak(stuttered_word) # Get the duration of the word's audio duration = len(normal_audio_data) / audio_rate # Calculate the start and end times of the stuttered audio start_time = 0 end_time = duration # Calculate the start and end frames of the stuttered audio start_frame = int(start_time audio_rate) end_frame = int(end_time * audio_rate) # Concatenate the audio data and append it to the list audio_data_list.append(stuttered_audio_data[start_frame:end_frame]) audio_data_list.append(repeat_audio_data[start_frame:end_frame]) # Move to the next word i += num_normal_words + 1 # Concatenate all the audio data and write it to a stream audio_data = np.concatenate(audio_data_list) player = pyaudio.PyAudio() stream = player.open(format=player.get_format_from_width(audio_format), channels=1, rate=audio_rate, output=True) stream.write(audio_data.tobytes()) stream.stop_stream() stream.close() player.terminate() def stutter_text_from_file(file_path): # Read the input text from the file with open(file_path, 'r') as f: text = f.read() # Split the text into sentences sentences = re.split('[.?!\n]', text) # Process each sentence for i, sentence in enumerate(sentences): # Check if this is the last sentence and it doesn't end with punctuation if i == len(sentences) - 1 and sentence.strip() != '': # Add a period to the end of the sentence sentence = sentence.strip() + '.' # Check if the sentence is not empty if len(sentence.strip()) > 0: stutter_text(sentence.strip()) if __name__ == '__main__': # Get the path to the input file file_path = 'readme.txt' # Stutter the text from the file stutter_text_from_file(file_path) --v View on YouTube -=-=- Check out my excellent homepage!
Fri, 10 Mar 2023, 07:10
Jayenkai	Sounding good, @Spinal! Now you need to draw her! @Pakz.. Yeah, I've now deleted Bing+Edge from my systems. They had it.. They really did.. For a good week or two, the whole internet was finally installing Edge and Bing was riding high on people's minds. Then they fucked it all up. Aw well. I've been using that poe thing you mentioned the other day. That's (so far!) been nice and swift, though.. as you say, it's definitely getting slower -=-=- ''Load, Next List!''
Fri, 10 Mar 2023, 12:13
spinal	ounding good, @spinal! Now you need to draw her! That's going to be the difficult part. So far though, a popular AI has created a reasonable imitation of a popular AI. -=-=- Check out my excellent homepage!
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