### Install Resemblyzer Package

Source: https://github.com/resemble-ai/resemblyzer/blob/master/README.md

Command to install the Resemblyzer library via pip for Python 3.5+ environments.

```bash
pip install resemblyzer
```

--------------------------------

### Speaker Diarization with Continuous Embeddings (Python)

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

This example demonstrates speaker diarization by identifying which speaker is active at any given moment in a recording using continuous embeddings. It involves loading a full audio recording, defining segments for reference speakers, creating reference speaker embeddings, and then computing continuous embeddings for the entire recording. Similarity is calculated between continuous partial embeddings and reference speaker embeddings to identify speaker transitions. The VoiceEncoder is initialized, and CPU is recommended for long audio due to high memory usage.

```python
from resemblyzer import VoiceEncoder, preprocess_wav, sampling_rate
from pathlib import Path
import numpy as np

encoder = VoiceEncoder("cpu")  # CPU recommended for long audio (high memory usage)

# Load the full audio recording
wav = preprocess_wav(Path("audio_data/X2zqiX6yL3I.mp3"))

# Extract reference segments for each speaker (start_time, end_time in seconds)
segments = [[0, 5.5], [6.5, 12], [17, 25]]
speaker_names = ["Speaker A", "Speaker B", "Speaker C"]

# Cut reference audio for each speaker
speaker_wavs = [
    wav[int(s[0] * sampling_rate):int(s[1] * sampling_rate)]
    for s in segments
]

# Create speaker reference embeddings
speaker_embeds = [encoder.embed_utterance(sw) for sw in speaker_wavs]

# Get continuous embeddings for the entire recording
# rate=16 means one embedding every ~0.0625 seconds
_, continuous_embeds, wav_splits = encoder.embed_utterance(
    wav,
    return_partials=True,
    rate=16
)
print(f"Generated {len(continuous_embeds)} continuous embeddings")

# Compute similarity of each continuous embedding against all speakers
similarity_dict = {}
for name, speaker_embed in zip(speaker_names, speaker_embeds):
    # Matrix multiplication: (n_frames, 256) @ (256,) = (n_frames,)
    similarity_dict[name] = continuous_embeds @ speaker_embed

```

--------------------------------

### Audio Preprocessing Pipeline

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

Demonstrates independent use of audio utilities including resampling, volume normalization, silence trimming, and mel spectrogram conversion.

```python
from resemblyzer import sampling_rate
from resemblyzer.audio import wav_to_mel_spectrogram, trim_long_silences, normalize_volume, preprocess_wav
import librosa

raw_wav, sr = librosa.load("audio_data/librispeech_test-other/1688/1688-142285-0000.flac", sr=None)
wav_resampled = librosa.resample(raw_wav, orig_sr=sr, target_sr=sampling_rate)
wav_normalized = normalize_volume(wav_resampled, target_dBFS=-30, increase_only=True)
wav_trimmed = trim_long_silences(wav_normalized)
mel = wav_to_mel_spectrogram(wav_trimmed)

wav_full = preprocess_wav("audio_data/librispeech_test-other/1688/1688-142285-0000.flac")
```

--------------------------------

### POST /preprocess-audio

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

Provides utility functions to prepare raw audio for analysis, including resampling, volume normalization, silence trimming, and mel spectrogram conversion.

```APIDOC
## POST /preprocess-audio

### Description
Transforms raw audio files into a standardized format suitable for voice encoder input. Includes volume normalization to a target dBFS and VAD-based silence trimming.

### Method
POST

### Endpoint
/preprocess-audio

### Parameters
#### Request Body
- **file_path** (string) - Required - Path to the input audio file.
- **target_dBFS** (integer) - Optional - Target volume level in dBFS.
- **trim_silence** (boolean) - Optional - Whether to remove long silences.

### Request Example
{
  "file_path": "audio/input.flac",
  "target_dBFS": -30,
  "trim_silence": true
}

### Response
#### Success Response (200)
- **mel_spectrogram** (array) - The resulting mel spectrogram data.
- **duration** (float) - Duration of the processed audio in seconds.

#### Response Example
{
  "mel_spectrogram": [[0.1, 0.2], [0.3, 0.4]],
  "duration": 4.5
}
```

--------------------------------

### Display Resemblyzer Model Hyperparameters (Python)

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

This Python code snippet imports and prints various hyperparameters from the resemblyzer.hparams module. It displays configurations for mel-filterbank, audio processing, VAD, and the model architecture, providing insights into the model's operational settings.

```python
from resemblyzer.hparams import (
    mel_window_length,
    mel_window_step,
    mel_n_channels,
    sampling_rate,
    partials_n_frames,
    vad_window_length,
    vad_moving_average_width,
    vad_max_silence_length,
    audio_norm_target_dBFS,
    model_hidden_size,
    model_embedding_size,
    model_num_layers,
)

print("Audio Processing Parameters:")
print(f"  Sampling rate: {sampling_rate} Hz")
print(f"  Mel window: {mel_window_length}ms length, {mel_window_step}ms step")
print(f"  Mel channels: {mel_n_channels}")
print(f"  Partial utterance duration: {partials_n_frames * mel_window_step}ms")

print("\nModel Architecture:")
print(f"  LSTM layers: {model_num_layers}")
print(f"  Hidden size: {model_hidden_size}")
print(f"  Embedding size: {model_embedding_size}")

print("\nVAD Parameters:")
print(f"  Window length: {vad_window_length}ms")
print(f"  Smoothing width: {vad_moving_average_width} frames")
print(f"  Max silence: {vad_max_silence_length} frames")
```

--------------------------------

### Generate Voice Embeddings with Resemblyzer

Source: https://github.com/resemble-ai/resemblyzer/blob/master/README.md

This snippet demonstrates how to initialize the VoiceEncoder, preprocess an audio file, and generate a 256-dimensional embedding vector representing the voice characteristics.

```python
from resemblyzer import VoiceEncoder, preprocess_wav
from pathlib import Path
import numpy as np

fpath = Path("path_to_an_audio_file")
wav = preprocess_wav(fpath)

encoder = VoiceEncoder()
embed = encoder.embed_utterance(wav)
np.set_printoptions(precision=3, suppress=True)
print(embed)
```

--------------------------------

### Preprocess Audio Waveforms

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

The preprocess_wav function prepares raw audio for the encoder by resampling to 16kHz, normalizing volume, and trimming silence. It accepts file paths or numpy arrays as input.

```python
from resemblyzer import preprocess_wav
from pathlib import Path
import numpy as np

# Load and preprocess audio from file path
wav = preprocess_wav("audio_data/speech.mp3")

# Load from Path object
wav = preprocess_wav(Path("audio_data/librispeech_test-other/1688/1688-142285-0000.flac"))

# Preprocess existing numpy array
raw_audio = np.random.randn(32000).astype(np.float32)
wav = preprocess_wav(raw_audio, source_sr=16000)

# Preprocess with automatic resampling
raw_audio_44k = np.random.randn(88200).astype(np.float32)
wav = preprocess_wav(raw_audio_44k, source_sr=44100)
```

--------------------------------

### Detect Fake Speech via Similarity Thresholding

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

Compares unknown audio samples against authentic ground truth embeddings. It calculates similarity scores and applies a threshold to classify audio as real or fake.

```python
from resemblyzer import VoiceEncoder, preprocess_wav
from pathlib import Path
import numpy as np

encoder = VoiceEncoder()
data_dir = Path("audio_data/donald_trump")
real_wavs = [preprocess_wav(f) for f in data_dir.glob("real/*.mp3")]
fake_wavs = [preprocess_wav(f) for f in data_dir.glob("fake/*.mp3")]

gt_embeds = np.array([encoder.embed_utterance(wav) for wav in real_wavs[:6]])
test_wavs = real_wavs[6:] + fake_wavs
test_labels = ["real"] * len(real_wavs[6:]) + ["fake"] * len(fake_wavs)
test_embeds = np.array([encoder.embed_utterance(wav) for wav in test_wavs])

similarity_scores = (gt_embeds @ test_embeds.T).mean(axis=0)
FAKE_DETECTION_THRESHOLD = 0.84

for i, (score, label) in enumerate(zip(similarity_scores, test_labels)):
    prediction = "REAL" if score > FAKE_DETECTION_THRESHOLD else "FAKE"
    print(f"Sample {i+1}: score={score:.3f}, actual={label}, predicted={prediction}")
```

--------------------------------

### Initialize VoiceEncoder Model

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

The VoiceEncoder class loads the pretrained neural network. It supports automatic device selection (CUDA/CPU), custom weight loading, and configurable verbosity.

```python
from resemblyzer import VoiceEncoder
import torch

# Default initialization - auto-selects CUDA or CPU
encoder = VoiceEncoder()

# Force CPU usage
encoder_cpu = VoiceEncoder("cpu")

# Force specific GPU
encoder_gpu = VoiceEncoder("cuda:0")

# Silent initialization
encoder_silent = VoiceEncoder(verbose=False)

# Load custom trained weights
encoder_custom = VoiceEncoder(weights_fpath="/path/to/custom_model.pt")
```

--------------------------------

### Compute Voice Similarity using Embeddings (Python)

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

This snippet demonstrates how to compute voice similarity between speaker embeddings using the dot product, as embeddings are L2-normalized. It involves loading audio files, computing utterance embeddings using VoiceEncoder, and then calculating similarity scores. The code also shows batch similarity computation using matrix multiplication and applying a verification threshold for speaker verification. Inputs are audio file paths, and outputs are similarity scores.

```python
from resemblyzer import VoiceEncoder, preprocess_wav
from pathlib import Path
import numpy as np

encoder = VoiceEncoder()

# Load utterances from two different speakers
speaker1_wav = preprocess_wav(Path("audio_data/librispeech_test-other/1688/1688-142285-0000.flac"))
speaker2_wav = preprocess_wav(Path("audio_data/librispeech_test-other/1998/1998-15444-0000.flac"))

# Another utterance from speaker 1
speaker1_wav2 = preprocess_wav(Path("audio_data/librispeech_test-other/1688/1688-142285-0001.flac"))

# Compute embeddings
embed1 = encoder.embed_utterance(speaker1_wav)
embed2 = encoder.embed_utterance(speaker2_wav)
embed1_b = encoder.embed_utterance(speaker1_wav2)

# Compute similarity using dot product (embeddings are L2-normalized)
same_speaker_similarity = np.dot(embed1, embed1_b)
diff_speaker_similarity = np.dot(embed1, embed2)

print(f"Same speaker similarity: {same_speaker_similarity:.4f}")    # Expected: ~0.85+
print(f"Different speaker similarity: {diff_speaker_similarity:.4f}")  # Expected: ~0.5-0.7

# Batch similarity computation using matrix multiplication
embeds_a = np.array([embed1, embed2])
embeds_b = np.array([embed1_b, embed2])
similarity_matrix = np.inner(embeds_a, embeds_b)
print(f"Similarity matrix:
{similarity_matrix}")

# Speaker verification with threshold
VERIFICATION_THRESHOLD = 0.80
is_same_speaker = same_speaker_similarity > VERIFICATION_THRESHOLD
print(f"Verified as same speaker: {is_same_speaker}")
```

--------------------------------

### Generate Speaker Embedding from Multiple Utterances (Python)

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

This method creates a robust speaker embedding by averaging embeddings from multiple audio samples of the same speaker. It uses the VoiceEncoder class and preprocess_wav function from the resemblyzer library. The input is a list of preprocessed audio waveforms, and the output is a single speaker embedding vector. This approach is more reliable than using single utterances for creating speaker profiles.

```python
from resemblyzer import VoiceEncoder, preprocess_wav
from pathlib import Path
import numpy as np

encoder = VoiceEncoder()

# Load multiple utterances from the same speaker
speaker_dir = Path("audio_data/librispeech_test-other/1688")
wav_files = list(speaker_dir.glob("*.flac"))

# Preprocess all audio files
wavs = [preprocess_wav(f) for f in wav_files[:5]]  # Use 5 utterances

# Create speaker embedding from multiple utterances
speaker_embed = encoder.embed_speaker(wavs)
print(f"Speaker embedding shape: {speaker_embed.shape}")  # (256,)

# Compare with individual utterance embeddings
individual_embeds = [encoder.embed_utterance(wav) for wav in wavs]
print(f"Individual embedding variance: {np.var(individual_embeds, axis=0).mean():.6f}")

# Speaker embedding is more stable for verification
# Using more utterances creates more reliable profiles
speaker_embed_3 = encoder.embed_speaker(wavs[:3])  # 3 utterances
speaker_embed_5 = encoder.embed_speaker(wavs[:5])  # 5 utterances

# Similarity between profiles (should be high)
similarity = np.dot(speaker_embed_3, speaker_embed_5)
print(f"Similarity between 3-utterance and 5-utterance profile: {similarity:.4f}")
```

--------------------------------

### POST /detect-fake-speech

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

Analyzes audio samples against known authentic reference samples to determine if the speech is real or AI-generated based on similarity thresholds.

```APIDOC
## POST /detect-fake-speech

### Description
Compares unknown audio against known authentic reference samples. The similarity score between fake/cloned speech and real speech is typically lower than between two real samples, enabling detection of AI-generated voices.

### Method
POST

### Endpoint
/detect-fake-speech

### Parameters
#### Request Body
- **real_samples** (array of audio files) - Required - List of authentic reference audio files.
- **test_samples** (array of audio files) - Required - List of unknown audio files to be classified.
- **threshold** (float) - Optional - Similarity threshold for classification (default: 0.84).

### Request Example
{
  "real_samples": ["path/to/real1.mp3", "path/to/real2.mp3"],
  "test_samples": ["path/to/unknown1.mp3"],
  "threshold": 0.84
}

### Response
#### Success Response (200)
- **results** (array) - List of objects containing score, label, and prediction status.

#### Response Example
{
  "results": [
    {
      "sample": "unknown1.mp3",
      "score": 0.82,
      "prediction": "FAKE"
    }
  ]
}
```

--------------------------------

### Generate Speaker Embedding from Multiple Utterances

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

This method creates a robust speaker embedding by averaging embeddings from multiple audio samples of the same speaker. This produces more reliable speaker profiles than single utterances, as it captures consistent voice characteristics across different speech samples.

```APIDOC
## embed_speaker - Generate Speaker Embedding from Multiple Utterances

### Description
Generates a robust speaker embedding by averaging embeddings from multiple audio samples of the same speaker. This method produces more reliable speaker profiles than single utterances by capturing consistent voice characteristics across different speech samples.

### Method
`embed_speaker`

### Parameters
- **wavs** (list of numpy arrays) - Required - A list of preprocessed audio waveforms (numpy arrays) from the same speaker.

### Request Example
```python
from resemblyzer import VoiceEncoder, preprocess_wav
from pathlib import Path

encoder = VoiceEncoder()

# Load multiple utterances from the same speaker
speaker_dir = Path("audio_data/librispeech_test-other/1688")
wav_files = list(speaker_dir.glob("*.flac"))

# Preprocess all audio files
wavs = [preprocess_wav(f) for f in wav_files[:5]]  # Use 5 utterances

# Create speaker embedding from multiple utterances
speaker_embed = encoder.embed_speaker(wavs)
print(f"Speaker embedding shape: {speaker_embed.shape}")
```

### Response
- **speaker_embed** (numpy array) - The generated speaker embedding vector (e.g., shape (256,)).

### Response Example
```
Speaker embedding shape: (256,)
```

### Notes
- Using more utterances generally leads to more reliable speaker profiles.
- The variance of individual utterance embeddings can indicate the consistency of a speaker's voice.
```

--------------------------------

### Identify Speakers from Audio Segments

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

Calculates speaker similarity scores over time frames to identify the most likely speaker. It uses a threshold to determine confidence levels and labels unknown speakers.

```python
frame_times = [((s.start + s.stop) / 2) / sampling_rate for s in wav_splits]

for i in range(0, len(frame_times), 50):
    similarities = [similarity_dict[name][i] for name in speaker_names]
    best_speaker_idx = np.argmax(similarities)
    best_similarity = similarities[best_speaker_idx]

    speaker = speaker_names[best_speaker_idx] if best_similarity > 0.65 else "Unknown"
    confidence = "confident" if best_similarity > 0.75 else "uncertain"
    print(f"Time {frame_times[i]:.1f}s: {speaker} ({confidence}, sim={best_similarity:.2f})")
```

--------------------------------

### Generate Audio Embeddings

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

The embed_utterance method computes a 256-dimensional vector representing the speaker's voice. It supports granular analysis via partial embeddings and configurable coverage settings.

```python
from resemblyzer import VoiceEncoder, preprocess_wav
from pathlib import Path
import numpy as np

encoder = VoiceEncoder()
wav = preprocess_wav(Path("audio_data/librispeech_test-other/1688/1688-142285-0000.flac"))

# Basic usage - get single embedding vector
embed = encoder.embed_utterance(wav)

# Return partial embeddings for continuous analysis
embed, partial_embeds, wav_slices = encoder.embed_utterance(
    wav,
    return_partials=True,
    rate=16
)

# Control final partial inclusion
embed = encoder.embed_utterance(wav, min_coverage=0.5)
```

--------------------------------

### Voice Similarity Computation

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

Computes voice similarity between embeddings using cosine similarity, which simplifies to a dot product since all embeddings are L2-normalized. Similarity values range from 0 to 1, where higher values indicate more similar voices.

```APIDOC
## Voice Similarity Computation

### Description
Computes voice similarity between speaker embeddings. Cosine similarity is used, which simplifies to a dot product because embeddings are L2-normalized. Similarity ranges from 0 to 1, with higher values indicating greater similarity. Typical thresholds for same-speaker verification are between 0.75 and 0.85.

### Method
`np.dot(embedding1, embedding2)` for individual similarity, or `np.inner(embeddings_a, embeddings_b)` for batch computation.

### Parameters
- **embedding1** (numpy array) - The first speaker embedding.
- **embedding2** (numpy array) - The second speaker embedding.
- **embeddings_a** (numpy array) - A matrix of speaker embeddings.
- **embeddings_b** (numpy array) - A matrix of speaker embeddings.

### Request Example
```python
from resemblyzer import VoiceEncoder, preprocess_wav
from pathlib import Path
import numpy as np

encoder = VoiceEncoder()

# Load utterances from two different speakers
speaker1_wav = preprocess_wav(Path("audio_data/librispeech_test-other/1688/1688-142285-0000.flac"))
speaker2_wav = preprocess_wav(Path("audio_data/librispeech_test-other/1998/1998-15444-0000.flac"))

# Another utterance from speaker 1
speaker1_wav2 = preprocess_wav(Path("audio_data/librispeech_test-other/1688/1688-142285-0001.flac"))

# Compute embeddings
embed1 = encoder.embed_utterance(speaker1_wav)
embed2 = encoder.embed_utterance(speaker2_wav)
embed1_b = encoder.embed_utterance(speaker1_wav2)

# Compute similarity using dot product
same_speaker_similarity = np.dot(embed1, embed1_b)
diff_speaker_similarity = np.dot(embed1, embed2)

print(f"Same speaker similarity: {same_speaker_similarity:.4f}")
print(f"Different speaker similarity: {diff_speaker_similarity:.4f}")

# Batch similarity computation
embeds_a = np.array([embed1, embed2])
embeds_b = np.array([embed1_b, embed2])
similarity_matrix = np.inner(embeds_a, embeds_b)
print(f"Similarity matrix:\n{similarity_matrix}")
```

### Response
- **similarity_score** (float) - A value between 0 and 1 indicating the similarity between two voice embeddings.
- **similarity_matrix** (numpy array) - A matrix where each element represents the similarity between corresponding embeddings in the input matrices.

### Response Example
```
Same speaker similarity: 0.9234
Different speaker similarity: 0.6123
Similarity matrix:
[[0.9234 0.5432]
 [0.5432 0.8876]]
```

### Notes
- A common threshold for speaker verification is 0.80.
```

--------------------------------

### Speaker Diarization

Source: https://context7.com/resemble-ai/resemblyzer/llms.txt

Speaker diarization identifies who speaks when in a recording by tracking active speakers. It works by computing similarity between reference speaker embeddings and continuous partial embeddings from the audio, allowing for the identification of speaker transitions.

```APIDOC
## Speaker Diarization - Identifying Who Speaks When

### Description
Performs speaker diarization to identify which speaker is active at any given moment in a recording. This is achieved by computing the similarity between reference speaker embeddings and continuous partial embeddings extracted from the audio, enabling the detection of speaker transitions.

### Method
`encoder.embed_utterance(wav, return_partials=True, rate=16)` for generating continuous embeddings, followed by similarity computations.

### Parameters
- **wav** (numpy array) - The preprocessed audio waveform for the entire recording.
- **return_partials** (bool) - If True, returns continuous embeddings.
- **rate** (int) - The rate at which to generate continuous embeddings (e.g., 16 means one embedding every ~0.0625 seconds).
- **segments** (list of lists) - A list of [start_time, end_time] pairs in seconds, defining reference audio segments for each speaker.
- **speaker_names** (list of strings) - Names corresponding to each speaker reference segment.

### Request Example
```python
from resemblyzer import VoiceEncoder, preprocess_wav, sampling_rate
from pathlib import Path
import numpy as np

encoder = VoiceEncoder("cpu")  # CPU recommended for long audio

# Load the full audio recording
wav = preprocess_wav(Path("audio_data/X2zqiX6yL3I.mp3"))

# Extract reference segments for each speaker (start_time, end_time in seconds)
segments = [[0, 5.5], [6.5, 12], [17, 25]]
speaker_names = ["Speaker A", "Speaker B", "Speaker C"]

# Cut reference audio for each speaker
speaker_wavs = [
    wav[int(s[0] * sampling_rate):int(s[1] * sampling_rate)]
    for s in segments
]

# Create speaker reference embeddings
speaker_embeds = [encoder.embed_utterance(sw) for sw in speaker_wavs]

# Get continuous embeddings for the entire recording
_, continuous_embeds, wav_splits = encoder.embed_utterance(
    wav,
    return_partials=True,
    rate=16
)
print(f"Generated {len(continuous_embeds)} continuous embeddings")

# Compute similarity of each continuous embedding against all speakers
similarity_dict = {}
for name, speaker_embed in zip(speaker_names, speaker_embeds):
    # Matrix multiplication: (n_frames, 256) @ (256,) = (n_frames,)
    similarity_dict[name] = continuous_embeds @ speaker_embed

# The similarity_dict now contains the similarity scores for each speaker over time.
# Further processing can be done to determine speaker turns.
```

### Response
- **continuous_embeds** (numpy array) - An array of embeddings representing segments of the audio.
- **wav_splits** (numpy array) - Time points corresponding to the `continuous_embeds`.
- **similarity_dict** (dict) - A dictionary where keys are speaker names and values are arrays of similarity scores over time for that speaker.

### Response Example
```
Generated 1500 continuous embeddings
```

### Notes
- Using the CPU is recommended for processing long audio files due to high memory usage.
- The `rate` parameter controls the granularity of the continuous embeddings.
```

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