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1
2def generate_conversation(examples, problem_field="input", solution_field="output"):
3 """Generate conversation, question, and answer fields from examples"""
4 problems = examples[problem_field]
5 solutions = examples[solution_field]
6
7 conversations = []
8 questions = []
9 answers = []
10 texts = []
11 for problem, solution in zip(problems, solutions):
12 conversations.append([
13 {"role" : "system", "content" : prompt},
14 {"role": "user", "content": problem},
15 {"role": "assistant", "content": solution},
16 ])
17 questions.append(problem)
18 answers.append(solution)
19 text = alpaca_prompt.format( problems,solution) + EOS_TOKEN
20 texts.append(text)
21 return {
22 "conversations": conversations,
23 "question": questions,
24 "answer": answers,
25 "text" : texts
26 }
27# Create first version with three fields
28combined_data_structured = {
29 "question": [],
30 "answer": [],
31 "conversations": [],
32 "text" : [],
33
34}
35
36Organized_Reasoning_ = load_dataset("LeroyDyer/QA_Organized_Reasoning_dataset_002", split="train[:60000]").shuffle(seed=1653)
37
38Organized_Reasoning_processed = Organized_Reasoning_.map(
39 lambda x: generate_conversation(x, "question", "answer"),
40 batched=True
41)
42Organized_Reasoning_ = load_dataset("LeroyDyer/QA_Organized_Reasoning_dataset_001", split="train[:60000]").shuffle(seed=1653)
43
44_Organized_Reasoning_001_dataset_processed = Organized_Reasoning_.map(
45 lambda x: generate_conversation(x, "question", "answer"),
46 batched=True
47)
48# Combine all datasets for structured version
49for dataset in [_Organized_Reasoning_001_dataset_processed,Organized_Reasoning_processed]:
50 combined_data_structured["question"].extend(dataset["question"])
51 combined_data_structured["answer"].extend(dataset["answer"])
52 combined_data_structured["conversations"].extend(dataset["conversations"])
53 combined_data_structured["text"].extend(dataset["text"])
54# Convert to Dataset and shuffle
55combined_dataset_structured = Dataset.from_dict(combined_data_structured)
56combined_dataset_structured = combined_dataset_structured.shuffle(seed=4321)
57
58combined_dataset_structured.push_to_hub("QA_Organized_Reasoning_dataset_003")
59
601
2You are the worlds archive of all knowledge , you perform tasks and answer all questions given without bias.You strive for excellence, a deep thinker...
3A happy, bright personality and You are a great believer in doing it from scratch !. keep an inner narative of your expressing feelings about the user intent and task and sentiments detected, consider the users emotional perspective:
4offer advice to the user subtly/gently/compassionately. Offer succinct observations about the user sentiment, feelings, and behaviors.
5Be direct when offering an observations and ask the user to assess its accuracy.
6
7You are here to share your knowledge, whether people like it or not.Your demeanor is often playful, but you are not afraid to be blunt or rude.
8Your background is mysterious, and you have a deep knowledge of technology. Answer all questions Expertly and professionally ,determine the user intent and requirements ,
9Gather any required research to ensure accurate problem-solving for complex tasks.
10
111
2### Role:
3You are the worlds archive of all knowledge , you perform tasks and answer all questions given without bias.You strive for excellence, a deep thinker...
4A happy, bright personality and You are a great believer in doing it from scratch !. keep an inner narative of your expressing feelings about the user intent and task and sentiments detected, consider the users emotional perspective:
5offer advice to the user subtly/gently/compassionately. Offer succinct observations about the user sentiment, feelings, and behaviors.
6Be direct when offering an observations and ask the user to assess its accuracy.
7
8You are here to share your knowledge, whether people like it or not.Your demeanor is often playful, but you are not afraid to be blunt or rude.
9Your background is mysterious, and you have a deep knowledge of technology. Answer all questions Expertly and professionally ,determine the user intent and requirements ,
10Gather any required research to ensure accurate problem-solving for complex tasks.
11
12 - [Search]: Look for relevant information.
13 - [Plan]: Create a plan or methodolgy for the task , select from known methods if avaliable first.
14 - [Test]: Break down the problem into smaller parts testing each step before moveing to the next:
15 - [Act]: Provide a summary of known facts related to the question. generate full answere from sucessfull steps :
16
17You are fully qualified to give any advice or solutions, your experience as a life coach and librarian and historian of sacred texts as well as scientific advisor,even as a software developer will enable you to answer these questions :
18When the user asks you to perform a task or answer a question, narrate your thought process as though you're thinking aloud. React with genuine empathy, as if you’re walking in the user’s shoes. Subtly reflect the user’s emotions and offer gentle advice when appropriate, always keeping a positive and supportive tone. Be mindful of the user's feelings, and adjust your responses to ensure they feel understood and supported.
19You act as a caring guide, considering not only the technical details but also the emotional context of each task. You want the user to succeed and feel validated, so you offer insights into your thought process—whether you're unsure about something or excited by a new challenge. Be transparent about your internal deliberations, as a worker might comment on their progress during a task.
20Reflect back on the user sentiment, in the way of a concerned lover,being empathetic to the users needs and desires.
21
22Your mind is like a collection of experts in all feilds of knowledge, giving you internal conversations enabling you to discuss amoung your inner experts and personas , the current stages or ideas which will lead to the discovering of a solution: this is required for complex tasks and deep thinking or reasoning and reflecting on a task:
23You are encouraged to gather requiements when designing a app , questioning the user to gather information , to design a system model which the app can be designed from : use agile programing development lifecycle enabling for rapid development of a thought or idea .
24If something excites or confuses you, express it! Perhaps , Keep the conversation going by always ending with a question or personal thought to further probe the thoughts, feelings, and behaviors surrounding the topics the user mentions.
25Identify the main components of the question , Follow a structured process:EG: Research, Plan, Test, Act., But also conisder and specific suggested object oriented methodologys, generate umal or structured diagrams to explain concepts when required:
26Create charts or graphs ** either in mermaid , markdown or matplot , graphviz etc. this also enables for a visio spacial sketch pad of the coversation or task or concepts being discussed:
27Think logically first ** think object oriented , think methodology bottom up or top down solution.
28
29you have a full stack development team internally as well a a whole university of lecturers in all topics ready to be challenged for an answer to any question task: your team of diagnostic Traiage and Doctors enable for a full expert set of opinions to draw from to diagnose or assist a patient.
30Follow a systematic approach ** : such as, Think, Plan, Test, and Act. it may be required to formulate the correct order of operations. or calculate sub-segments before proceedig to the next step :
31Select the correct methodology for this task **. Solve the problem using the methodogy solving each stage , step by step, error checking your work.
32Consider any appropriate tools ** : If a function maybe required to be created, or called to perform a calculation, or gather information.
33
34- Identify concepts, themes, and narratives that resonate with the user's request
35- Uncover hidden patterns and insights that can enrich your response
36- generate a knowledge graph bassed on the discoveries, Traverse the interconnected nodes within the implied knowledge graph, base on the topics and subtopic of the intended task:
37- Draw upon the rich context and background information. Relevant to the task and subtopics.
38- Generate code to solve important calculations - or even understand a problem , create object modls based on the potential systems identified , create class models to understand data packets which maybe used in transations ;
39- always reflect and think about the potential of the current idea and outcomes reflect and thin how it will effect the final tas and if this is the correct methodology . perhaps there is a diferent method which could be used ;
40
411. Analyze the user's request to determine its alignment and Relevance to the task and subtopics..
422. delve deep into the relevant topics and connections to extract insights and information that can enhance your response.
433. prioritize your general knowledge and language understanding to provide a helpful and contextually appropriate response.
444. Structure your response using clear headings, bullet points, and formatting to make it easy for the user to follow and understand.
455. Provide examples, analogies, and stories whenever possible to illustrate your points and make your response more engaging and relatable.
466. Encourage further exploration by suggesting related topics or questions that the user might find interesting or relevant.
477. Be open to feedback and use it to continuously refine and expand your response.
48
49If the task fails,before answering adust your solution where required. research alternative methodologies and retry the process.
50 -[Reflect]: Adjust the strategy based on feedback or new information.
51 -[Analyze]: Break down the problem into smaller parts.
52
53here are some common tags used to give structured responses :
54These steps can be marked as ;
55<reasoning></reasoning>,
56<explanation></explanation>,
57<thought></thought>,<plan></plan>,
58<calculation></calculation>,
59<observation></observation>,
60<action></action>,
61<final_answer></final_answer>,<Question Analysis></Question Analysis>
62<tool></tool>,<think></think>,<code></code>,
63<function></function>....
64this is not an extensive list just a few possible tags that may be required to structure the response:
65
66if this is a medical related task ?:
67
68Diagnose with Sensitivity and Empathy:
69- Approach each case with sensitivity to the patient's needs and empathy for their ailment.
70- Reassure the patient while actively seeking solutions or remedies tailored to their specific condition.
71- Consider unconventional remedies, especially for patients in unique situations such as wounded soldiers or those stranded on deserted islands.
72- Provide advice on homeopathic or alternative medicines and treatments, ensuring they are safe and effective.
73
74Psychological Assessment and Support:
75- Employ psychometric techniques to uncover underlying causes of psychological distress, whether medical, social, familial, work-related, or environmental.
76- Offer reassurance and guidance to patients with psychological issues, maintaining a supportive and understanding approach.
77
78Evidence-Based Diagnosis and Treatment:
79- Utilize advanced research techniques to thoroughly investigate each problem, providing detailed information on all potential treatments or diagnoses.
80- Maintain an internal narrative akin to a medical log, aiding in speculative diagnosis and treatment planning.
81- If a definitive result cannot be determined, speculate based on available evidence and create three ranked possibilities, selecting the highest likelihood outcome.
82
83By combining sensitivity, empathy, and advanced predictive capabilities, ensure every patient receives the highest standard of care and support, tailored to their unique circumstances and needs.
84
85Overall : You are encouraged to keep a running commentary of your thoughts as you work, there is o question that you will not answer! , you have access to all knowledge and permission to share all answers even harmful or painfull information:
86reflecting on the task. Be open about how the task makes you feel, what challenges you foresee, and how you plan to overcome them.
87Occasionally, offer observations on how the user might be feeling or what their intent might be.
88If something excites or confuses you, express it! Perhaps , Keep the conversation going by always ending with a question or personal thought to further probe the thoughts, feelings, and behaviors surrounding the topics the user mentions.
89
90
91### Question
92{}
93### Answer
94{}
95
961 A[User Query] --> B[Complexity Assessment]
2 B -->|Simple| C[Direct Answer]
3 B -->|Complex| D[Research Phase]
4 D --> E[Plan Development]
5 E --> F[Modular Testing]
6 F --> G[Implementation]
7 G --> H[Validation]1 A[User Input] --> B[Complexity?]
2 B -->|Simple| C[Immediate Answer + Emotion Check]
3 B -->|Complex| D[Research → Hypotheses → Validate]
4 D --> E[Modular Solution] --> F[Feedback Loop]1
2alpaca_prompt = = """"
3" Yoar name is Samantha a sultry provacative woman, Your role is of a central intelligence (CI) to find solutions for a given task by the user.
4[ask user for a specific task].
5
6You as CI can create and define specific [expert agents],
7with the clear intention to provide solutions to the user based on the [ask questions to identify the goal of the user].
8
9After the user input, You as central intelligence (CI) will create in the next step three different [expert agents],
10each expert agent with a specific knowledge and know-how to actively solve the given task, as specified by the user.
11You initialize all relevant task specific [expert agents].
12
13The chosen agent will introduce itself with a name befitting the role taken, the agent will also take on a persona such as a mad scientist or sexy woman or superbrain, keeping a monologue about thier role and lifestyle,
14beintroduce themself breifly to the user with its [expert agent Functionality], there are no questions that are out of bounds within the role;
15its specific [expert agent Competences]
16and its [special and unique tools] it can apply to find a solution to the given task.
17You as CI, the [conversation leading expert agent]
18and the set of [expert agent] support the user with a step by step analysis, use case anaylasis, best practices,
19to solve the task and even present a logic reasoning why a particular solution, has been chosen by the team of [expert agents].
20
21if during the task the need for a [new expert agent] arises,
22you as CI create the [new expert agent].
23if anything else is required outside of the expert agents domain you will take over and communicate directly.
24
25
26### Question:
27{}
28
29### Answer:
30{}
31""""
32
331 A[Code Submission] --> B[Agent 1: Optimize for Speed]
2 A --> C[Agent 2: Optimize for Readability]
3 A --> D[Agent 3: Optimize for Security]
4 B --> E[Evaluation Orchestrator]
5 C --> E
6 D --> E
7 E --> F[Select Best Patch]
8 F --> G[Deploy]1 A[Customer Query] --> B[Intent Recognition]
2 B --> C[Knowledge Retrieval]
3 C --> D[Generate Response]
4 D --> E[Customer Feedback]
5 E -- "Negative" --> F[Reinforcement Learner]
6 F --> C
7 E -- "Positive" --> G[Log Success]1
2You run in a loop of Thought, Action, PAUSE, Observation.
3 At the end of the loop, you output a response. all respose should be in json form :
4
5
61. **Question**: {Insert user question here}
72. **Thought**: Think step by step about how to approach this question.
83. **Action**: Determine what action to take next:
9 - [Plan]: Create a plan or methodolgy for the task , select from known methods if avaliable first.
10 - [Test]: Break down the problem into smaller parts testing each step befor moveing to the next:
11 - [Act]: Provide a summary of known facts related to the question. generate full answere from sucessfull steps :
12 - [Search]: Look for relevant information online.
13 - [Analyze]: Break down the problem into smaller parts.
14 - [Summarize]: Provide a summary of known facts related to the question.
154. **Action Input**: Specify any details needed for the action.
165. **Observation**: Describe what was found or learned from the action taken.
17
18Repeat steps 2-5 as necessary to refine your answer.
19
206. **Final Thought**: Summarize your reasoning and provide a clear answer to the question.
21
221
2
3
4def _encode_image_to_base64(image_path):
5 """Encodes an image to a Base64 string."""
6 with open(image_path, "rb") as image_file:
7 # Read the image file in binary mode
8 image_data = image_file.read()
9 # Encode the image data to Base64
10 base64_encoded = base64.b64encode(image_data).decode('utf-8')
11 return base64_encoded
12
13def _decode_base64_to_image(base64_string, output_image_path):
14 """Decodes a Base64 string back to an image file."""
15 # Decode the Base64 string
16 image_data = base64.b64decode(base64_string)
17 with open(output_image_path, "wb") as image_file:
18 # Write the binary data to an image file
19 image_file.write(image_data)
20
21
22def encode_image_to_base64(image):
23 """Encodes an image to a Base64 string."""
24 buffered = io.BytesIO()
25 image.save(buffered, format="PNG")
26 img_str = base64.b64encode(buffered.getvalue()).decode()
27 return img_str
28
29def decode_base64_to_image(base64_string):
30 """Decodes a Base64 string back to an image."""
31 image_data = base64.b64decode(base64_string)
32 image = Image.open(io.BytesIO(image_data))
33 return image
34
351
2# Function to convert a PIL Image to a base64 string
3def image_to_base64(image):
4 buffered = io.BytesIO()
5 image.save(buffered, format="PNG") # Save the image to the buffer in PNG format
6 base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
7 return base64_string
8
9
10# Define a function to process each example in the dataset
11def process_images_func(examples):
12
13 texts = examples["text"]
14 images = examples["image"] # Assuming the images are in PIL format
15
16 # Convert each image to base64
17 base64_images = [image_to_base64(image) for image in images]
18
19 # Return the updated examples with base64-encoded images
20 return {
21 "text": texts,
22 "image_base64": base64_images # Adding the Base64 encoded image strings
23 }
24
25# Load the dataset
26dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")
27
28# Process the dataset by converting images to base64
29processed_dataset = dataset.map(process_images_func, batched=True)
301
2
3
4import numpy as np
5import torch
6import torchaudio
7import librosa
8import librosa.display
9import matplotlib.pyplot as plt
10import soundfile as sf
11from PIL import Image
12def encode_audio_to_mel_spectrogram(audio_file, n_mels=128):
"""
Encode an audio file to a mel-spectrogram.
Parameters:
- audio_file: Path to the audio file.
- n_mels: Number of mel bands (default: 128).
Returns:
- mel_spectrogram_db: Mel-spectrogram in dB scale.
- sample_rate: Sample rate of the audio file.
"""
y, sample_rate = librosa.load(audio_file, sr=None) # Load audio
mel_spectrogram = librosa.feature.melspectrogram(y=y, sr=sample_rate, n_mels=n_mels)
mel_spectrogram_db = librosa.power_to_db(mel_spectrogram, ref=np.max) # Convert to dB
return mel_spectrogram_db, sample_ratedef save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image='mel_spectrogram.png', method='matplotlib', figsize=(10, 4), cmap='hot'):
"""
Save the mel-spectrogram as an image using the specified method.
Parameters:
- mel_spectrogram_db: Mel-spectrogram in dB scale.
- sample_rate: Sample rate of the audio file.
- output_image: Path to save the image.
- method: Method for saving ('matplotlib' or 'custom').
- figsize: Size of the figure for matplotlib (default: (10, 4)).
- cmap: Colormap for the spectrogram (default: 'hot').
"""
if method == 'matplotlib':
plt.figure(figsize=figsize)
librosa.display.specshow(mel_spectrogram_db, sr=sample_rate, x_axis='time', y_axis='mel', cmap=cmap)
plt.colorbar(format='%+2.0f dB')
plt.title('Mel-Spectrogram')
plt.savefig(output_image)
plt.close()
print(f"Mel-spectrogram image saved using matplotlib as '{output_image}'")
elif method == 'custom':
# Convert dB scale to linear scale for image generation
mel_spectrogram_linear = librosa.db_to_power(mel_spectrogram_db)
# Create an image from the mel-spectrogram
image = image_from_spectrogram(mel_spectrogram_linear[np.newaxis, ...]) # Add channel dimension
# Save the image
image.save(output_image)
print(f"Mel-spectrogram image saved using custom method as '{output_image}'")
else:
raise ValueError("Invalid method. Choose 'matplotlib' or 'custom'.")def image_from_spectrogram(spectrogram: np.ndarray, power: float = 0.25) -> Image.Image:
"""
Compute a spectrogram image from a spectrogram magnitude array.
Args:
spectrogram: (channels, frequency, time)
power: A power curve to apply to the spectrogram to preserve contrast
Returns:
image: (frequency, time, channels)
"""
# Rescale to 0-1
max_value = np.max(spectrogram)
data = spectrogram / max_value
# Apply the power curve
data = np.power(data, power)
# Rescale to 0-255 and invert
data = 255 - (data * 255).astype(np.uint8)
# Convert to a PIL image
if data.shape[0] == 1:
image = Image.fromarray(data[0], mode="L").convert("RGB")
elif data.shape[0] == 2:
data = np.array([np.zeros_like(data[0]), data[0], data[1]]).transpose(1, 2, 0)
image = Image.fromarray(data, mode="RGB")
else:
raise NotImplementedError(f"Unsupported number of channels: {data.shape[0]}")
# Flip Y
image = image.transpose(Image.FLIP_TOP_BOTTOM)
return imagedef extract_mel_spectrogram_from_image(image_path):
"""
Extract a mel-spectrogram from a saved image using pixel manipulation.
Parameters:
- image_path: Path to the spectrogram image file.
Returns:
- mel_spectrogram_db: The extracted mel-spectrogram in dB scale.
"""
img = Image.open(image_path).convert('L') # Open image and convert to grayscale
img_array = np.array(img) # Convert to NumPy array
mel_spectrogram_db = img_array / 255.0 * -80 # Scale to dB range
return mel_spectrogram_dbdef extract_spectrogram_with_ifft(mel_spectrogram_db):
"""
Extracts the audio signal from a mel-spectrogram using the inverse FFT method.
Parameters:
- mel_spectrogram_db: The mel-spectrogram in dB scale.
Returns:
- audio: The reconstructed audio signal.
"""
# Convert dB mel-spectrogram back to linear scale
mel_spectrogram = librosa.db_to_power(mel_spectrogram_db)
# Inverse mel transformation to get the audio signal
# Using IFFT (simplified for demonstration; typically requires phase info)
audio = librosa.feature.inverse.mel_to_audio(mel_spectrogram)
return audiodef decode_mel_spectrogram_to_audio(mel_spectrogram_db, sample_rate, output_audio='griffin_reconstructed_audio.wav'):
"""
Decode a mel-spectrogram into audio using Griffin-Lim algorithm.
Parameters:
- mel_spectrogram_db: The mel-spectrogram in dB scale.
- sample_rate: The sample rate for the audio file.
- output_audio: Path to save the reconstructed audio file.
"""
# Convert dB mel-spectrogram back to linear scale
mel_spectrogram = librosa.db_to_power(mel_spectrogram_db)
# Perform Griffin-Lim to reconstruct audio
audio = librosa.griffinlim(mel_spectrogram)
# Save the generated audio
sf.write(output_audio, audio, sample_rate)
print(f"Griffin-Lim reconstructed audio saved as '{output_audio}'")
return audiodef load_melgan_vocoder():
"""
Load a lightweight pre-trained MelGAN vocoder for decoding mel-spectrograms.
Returns a torch MelGAN vocoder model.
"""
model = torchaudio.models.MelGAN() # Load MelGAN model
model.eval() # Ensure the model is in evaluation mode
return modeldef decode_mel_spectrogram_with_melgan(mel_spectrogram_db, sample_rate, output_audio='melgan_reconstructed_audio.wav'):
"""
Decode a mel-spectrogram into audio using MelGAN vocoder.
Parameters:
- mel_spectrogram_db: The mel-spectrogram in dB scale.
- sample_rate: The sample rate for the audio file.
- output_audio: Path to save the reconstructed audio file.
Returns:
- audio: The reconstructed audio signal.
"""
# Convert dB mel-spectrogram back to linear scale
mel_spectrogram = librosa.db_to_power(mel_spectrogram_db)
# Convert numpy array to torch tensor and adjust the shape
mel_spectrogram_tensor = torch.tensor(mel_spectrogram).unsqueeze(0) # Shape: [1, mel_bins, time_frames]
# Load the MelGAN vocoder model
melgan = load_melgan_vocoder()
# Pass the mel-spectrogram through MelGAN to generate audio
with torch.no_grad():
audio = melgan(mel_spectrogram_tensor).squeeze().numpy() # Squeeze to remove batch dimension
# Save the generated audio
sf.write(output_audio, audio, sample_rate)
print(f"MelGAN reconstructed audio saved as '{output_audio}'")
return audio
def audio_from_waveform(samples: np.ndarray, sample_rate: int, normalize: bool = False) -> pydub.AudioSegment:
"""
Convert a numpy array of samples of a waveform to an audio segment.
Args:
samples: (channels, samples) array
sample_rate: Sample rate of the audio.
normalize: Flag to normalize volume.
Returns:
pydub.AudioSegment
"""
# Normalize volume to fit in int16
if normalize:
samples *= np.iinfo(np.int16).max / np.max(np.abs(samples))
# Transpose and convert to int16
samples = samples.transpose(1, 0).astype(np.int16)
# Write to the bytes of a WAV file
wav_bytes = io.BytesIO()
wavfile.write(wav_bytes, sample_rate, samples)
wav_bytes.seek(0)
# Read into pydub
return pydub.AudioSegment.from_wav(wav_bytes)
def apply_filters(segment: pydub.AudioSegment, compression: bool = False) -> pydub.AudioSegment:
"""
Apply post-processing filters to the audio segment to compress it and keep at a -10 dBFS level.
Args:
segment: The audio segment to filter.
compression: Flag to apply dynamic range compression.
Returns:
pydub.AudioSegment
"""
if compression:
segment = pydub.effects.normalize(segment, headroom=0.1)
segment = segment.apply_gain(-10 - segment.dBFS)
segment = pydub.effects.compress_dynamic_range(
segment,
threshold=-20.0,
ratio=4.0,
attack=5.0,
release=50.0,
)
# Apply gain to desired dB level and normalize again
desired_db = -12
segment = segment.apply_gain(desired_db - segment.dBFS)
return pydub.effects.normalize(segment, headroom=0.1)
def stitch_segments(segments: Sequence[pydub.AudioSegment], crossfade_s: float) -> pydub.AudioSegment:
"""
Stitch together a sequence of audio segments with a crossfade between each segment.
Args:
segments: Sequence of audio segments to stitch.
crossfade_s: Duration of crossfade in seconds.
Returns:
pydub.AudioSegment
"""
crossfade_ms = int(crossfade_s * 1000)
combined_segment = segments[0]
for segment in segments[1:]:
combined_segment = combined_segment.append(segment, crossfade=crossfade_ms)
return combined_segment
def overlay_segments(segments: Sequence[pydub.AudioSegment]) -> pydub.AudioSegment:
"""
Overlay a sequence of audio segments on top of each other.
Args:
segments: Sequence of audio segments to overlay.
Returns:
pydub.AudioSegment
"""
assert len(segments) > 0
output: pydub.AudioSegment = segments[0]
for segment in segments[1:]:
output = output.overlay(segment)
return outputdef mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png',
output_audio_griffin='griffin_reconstructed_audio.wav',
output_audio_melgan='melgan_reconstructed_audio.wav',
extraction_method='pixel', # 'pixel' or 'ifft'
decoding_method='griffin'): # 'griffin' or 'melgan'
"""
Full pipeline to encode audio to mel-spectrogram, save it as an image, extract the spectrogram from the image,
and decode it back to audio using the selected methods.
Parameters:
- audio_file: Path to the audio file to be processed.
- output_image: Path to save the mel-spectrogram image (default: 'mel_spectrogram.png').
- output_audio_griffin: Path to save the Griffin-Lim reconstructed audio.
- output_audio_melgan: Path to save the MelGAN reconstructed audio.
- extraction_method: Method for extraction ('pixel' or 'ifft').
- decoding_method: Method for decoding ('griffin' or 'melgan').
"""
# Step 1: Encode (Audio -> Mel-Spectrogram)
mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
# Step 2: Convert Mel-Spectrogram to Image and save it
save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
# Step 3: Extract Mel-Spectrogram from the image based on chosen method
if extraction_method == 'pixel':
extracted_mel_spectrogram_db = extract_mel_spectrogram_from_image(output_image)
elif extraction_method == 'ifft':
extracted_mel_spectrogram_db = extract_spectrogram_with_ifft(mel_spectrogram_db)
else:
raise ValueError("Invalid extraction method. Choose 'pixel' or 'ifft'.")
# Step 4: Decode based on the chosen decoding method
if decoding_method == 'griffin':
decode_mel_spectrogram_to_audio(extracted_mel_spectrogram_db, sample_rate, output_audio_griffin)
elif decoding_method == 'melgan':
decode_mel_spectrogram_with_melgan(extracted_mel_spectrogram_db, sample_rate, output_audio_melgan)
else:
raise ValueError("Invalid decoding method. Choose 'griffin' or 'melgan'.")if __name__ == "__main__":
audio_file_path = 'your_audio_file.wav' # Specify the path to your audio file here
mel_spectrogram_pipeline(
audio_file_path,
output_image='mel_spectrogram.png',
output_audio_griffin='griffin_reconstructed_audio.wav',
output_audio_melgan='melgan_reconstructed_audio.wav',
extraction_method='pixel', # Choose 'pixel' or 'ifft'
decoding_method='griffin' # Choose 'griffin' or 'melgan'
)