Add creak manipulation

.gitattributes

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+]

Dataset/Embeddings/std.json

@@ -0,0 +1,258 @@
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+]

Dataset/dataset.yaml

@@ -0,0 +1,67 @@
+dataset:
+  '7190_90542_000054_000000':
+    speaker_id: '7190'
+    example_id: '7190_90542_000054_000000'
+  '4830_25904_000008_000001':
+    speaker_id: '4830'
+    example_id: '4830_25904_000008_000001'
+  '8820_294120_000011_000001':
+    speaker_id: '8820'
+    example_id: '8820_294120_000011_000001'
+  '3009_10327_000027_000005':
+    speaker_id: '3009'
+    example_id: '3009_10327_000027_000005'
+  '7226_86965_000020_000001':
+    speaker_id: '7226'
+    example_id: '7226_86965_000020_000001'
+  '329_861_000024_000003':
+    speaker_id: '329'
+    example_id: '329_861_000024_000003'
+  '5802_76044_000038_000000':
+    speaker_id: '5802'
+    example_id: '5802_76044_000038_000000'
+  '1535_141644_000004_000001':
+    speaker_id: '1535'
+    example_id: '1535_141644_000004_000001'
+  '7011_66622_000032_000002':
+    speaker_id: '7011'
+    example_id: '7011_66622_000032_000002'
+  '8758_296465_000020_000000':
+    speaker_id: '8758'
+    example_id: '8758_296465_000020_000000'
+  '1034_121119_000028_000001':
+    speaker_id: '1034'
+    'example_id': '1034_121119_000028_000001'
+  '4957_30119_000070_000001':
+    speaker_id: '4957'
+    example_id: '4957_30119_000070_000001'
+  '83_9960_000017_000003':
+    speaker_id: '83'
+    example_id: '83_9960_000017_000003'
+  '7059_77897_000017_000001':
+    speaker_id: '7059'
+    example_id: '7059_77897_000017_000001'
+  '1731_142320_000122_000005':
+    speaker_id: '1731'
+    example_id: '1731_142320_000122_000005'
+  '6918_47541_000006_000008':
+    speaker_id: '6918'
+    example_id: '6918_47541_000006_000008'
+  '6544_71420_000024_000001':
+    speaker_id: '6544'
+    example_id: '6544_71420_000024_000001'
+  '7245_104888_000016_000000':
+    speaker_id: '7245'
+    example_id: '7245_104888_000016_000000'
+  '5012_80192_000020_000003':
+    speaker_id: '5012'
+    example_id: '5012_80192_000020_000003'
+  '1422_149735_000006_000000':
+    speaker_id: '1422'
+    example_id: '1422_149735_000006_000000'
+  '14_212_000019_000000':
+    speaker_id: '14'
+    example_id: '14_212_000019_000000'
+  '1088_129236_000006_000007':
+    speaker_id: '1088'
+    example_id: '1088_129236_000006_000007'

app.py

@@ -1,31 +1,39 @@
 import numpy as np 
 from pathlib import Path
-import padertorch as pt
 import paderbox as pb
-import time
 import torch
-import torchaudio
 from onnxruntime import InferenceSession
 from pvq_manipulation.models.vits import Vits_NT
 from pvq_manipulation.models.ffjord import FFJORD
-from IPython.display import display, Audio, clear_output
 from pvq_manipulation.models.hubert import HubertExtractor, SID_LARGE_LAYER
 import librosa
 from pvq_manipulation.helper.vad import EnergyVAD
 import gradio as gr
+from pvq_manipulation.helper.creapy_wrapper import process_file
 
-device = 'cpu'  #'cuda' if torch.cuda.is_available() else 'cpu'
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+pvq_labels = ['Weight', 'Resonance', 'Breathiness', 'Roughness', 'Loudness', 'Strain', 'Pitch']
 
-# load tts model
-storage_dir_tts = Path("./models/tts_model/")
-tts_model = Vits_NT.load_model(storage_dir_tts, "model.pt")
+dataset_dict = pb.io.load_yaml('./Dataset/dataset.yaml')
+
+cached_example_id = None
+cached_loaded_example = None
+cached_labels = None
+cached_d_vector = None
+cached_unmanipulated = None
+
+# path to stats
+stats_path = Path('./Dataset/Embeddings/')
 
 # load normalizing flow
 storage_dir_normalizing_flow = Path("./models/norm_flow")
-speaker_conditioning = pb.io.load(storage_dir_normalizing_flow / "speaker_conditioning.json")
-
+config_norm_flow = pb.io.load_yaml(storage_dir_normalizing_flow / "config.json")
 normalizing_flow = FFJORD.load_model(storage_dir_normalizing_flow, checkpoint="model.pt", device=device)
 
+# load tts model
+storage_dir_tts = Path("./models/tts_model/")
+tts_model = Vits_NT.load_model(storage_dir_tts, "model.pt")
+
 # load hubert features model
 hubert_model = HubertExtractor(
     layer=SID_LARGE_LAYER,
@@ -35,140 +43,157 @@ hubert_model = HubertExtractor(
     # storage_dir= # target storage dir hubert model
 )
 
-# example synthesis
-# speaker_id = 1034
-# example_id = "1034_121119_000028_000001"
-
-# wav_1 = tts_model.synthesize_from_example({
-#     'text' : "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", 
-#     'd_vector_storage_root': f"./Saved_models/Dataset/Embeddings/{speaker_id}/{example_id}.pth"
-# })
-# display(Audio(wav_1, rate=24_000, normalize=True))
 
-# manipulation block
 def get_manipulation(
-    d_vector,
+    example,
     labels,
-    flow, 
+    flow,
     tts_model,
+    d_vector,
+    config_norm_flow,
     manipulation_idx=0,
     manipulation_fkt=1,
 ):
     labels_manipulated = labels.clone()
-    labels_manipulated[:,manipulation_idx] += manipulation_fkt
-    
-    output_forward = flow.forward((d_vector.float(), labels))[0]
+    labels_manipulated[:, manipulation_idx] += manipulation_fkt
+
+    if config_norm_flow['flag_remove_mean']:
+        global_mean = pb.io.load(stats_path / "mean.json")
+        global_mean = torch.tensor(global_mean, dtype=torch.float32)
+        speaker_embedding_norm = (d_vector - global_mean)
+        global_std = pb.io.load(stats_path / "std.json")
+        global_std = torch.tensor(global_std, dtype=torch.float32)
+        speaker_embedding_norm = speaker_embedding_norm / global_std
+    else:
+        speaker_embedding_norm = d_vector
+
+    output_forward = flow.forward((speaker_embedding_norm.float(), labels))[0]
     sampled_class_manipulated = flow.sample((output_forward, labels_manipulated))[0]
 
+    if config_norm_flow['flag_remove_mean']:
+        sampled_class_manipulated = (sampled_class_manipulated * global_std + global_mean)
+
     wav = tts_model.synthesize_from_example({
-        'text': "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
+        'text': example['transcription'],
         'd_vector': d_vector.detach().numpy(),
         'd_vector_man': sampled_class_manipulated.detach().numpy(),
-    })    
+        'd_vector_storage_root': example['d_vector_storage_root'],
+    })
     return wav
 
-def extract_speaker_embedding(example):
-    observation, sr = pb.io.load_audio(example['audio_path']['observation'], return_sample_rate=True)
-    observation = librosa.resample(observation, orig_sr=sr, target_sr=16_000)
-    
-    vad = EnergyVAD(sample_rate=16_000)
-    if observation.ndim == 1:
-        observation = observation[None, :]
-    
-    observation = vad({'audio_data': observation})['audio_data']
-    
-    with torch.no_grad():
-        example = tts_model.speaker_manager.prepare_example({'audio_data': {'observation': observation}, **example})
-        example = pt.data.utils.collate_fn([example])
-        example['features'] = torch.tensor(np.array(example['features']))
-        d_vector = tts_model.speaker_manager.forward(example)[0]
-    return d_vector
-
-# load speaker labels
-def load_speaker_labels(example, speaker_conditioning, reg_stor_dir=Path('./models/pvq_extractor/')):
-    audio, _ = torchaudio.load(example['audio_path']['observation'])
-    audio = audio.to(device)
-    num_samples = torch.tensor([audio.shape[-1]], device=device)
 
+def get_creak_label(example):
+    audio_data = example['loaded_audio_data']['16_000']
+    test, y_pred, included_indices = process_file(audio_data)
+    mean_creak = np.mean(y_pred[included_indices])
+    return mean_creak * 100
+
+
+def load_speaker_labels(example, reg_stor_dir=Path('./models/pvq_extractor/')):
+    audio_data = torch.tensor(example['loaded_audio_data']['16_000'], dtype=torch.float)[None, :]
+    num_samples = torch.tensor([audio_data.shape[-1]])
+
+    if torch.cuda.is_available():
+        audio_data = audio_data.cuda()
+        num_samples = num_samples.cuda()
     providers = ["CPUExecutionProvider"]
 
     with torch.no_grad():
         features, seq_len = hubert_model(
-            audio, 
-            24_000, 
+            audio_data,
+            16_000,
             sequence_lengths=num_samples,
         )
         features = np.mean(features.squeeze(0).detach().cpu().numpy(), axis=-1)
-
         pvqd_predictions = {}
-        for pvq in ['Breathiness', 'Loudness', 'Pitch', 'Resonance', 'Roughness', 'Strain', 'Weight']:
+        for pvq in pvq_labels:
             with open(reg_stor_dir / f"{pvq}.onnx", "rb") as fid:
                 onnx = fid.read()
             sess = InferenceSession(onnx, providers=providers)
             pred = sess.run(None, {"X": features[None]})[0].squeeze(1)
             pvqd_predictions[pvq] = pred.tolist()[0]
-    labels = []
-    for key in speaker_conditioning:
-        labels.append(pvqd_predictions[key]/100)
-    return torch.tensor(labels)
 
+    pvqd_predictions['Creak_mean'] = get_creak_label(example)
+    labels = [pvqd_predictions[key] / 100 for key in pvq_labels + ["Creak_mean"]]
+    return torch.tensor(labels, device=device).float()
+
+
+def load_audio_files(example):
+    observation_loaded, sr = pb.io.load_audio(example['audio_path']['observation'], return_sample_rate=True)
+
+    example['loaded_audio_data'] = {}
+    observation = librosa.resample(observation_loaded, orig_sr=sr, target_sr=16_000)
+
+    vad = EnergyVAD(sample_rate=16_000)
+    if observation.ndim == 1:
+        observation = observation[None, :]
+
+    observation = vad({'audio_data': observation})['audio_data']
+    example['loaded_audio_data']['16_000'] = observation
+
+    observation = librosa.resample(observation, orig_sr=sr, target_sr=24_000)
+    vad = EnergyVAD(sample_rate=24_000)
+    if observation.ndim == 1:
+        observation = observation[None, :]
+    observation = vad({'audio_data': observation})['audio_data']
+    example['loaded_audio_data']['24_000'] = observation
+    return example
 
-example = {
-    'audio_path': {'observation': "audio/1034_121119_000028_000001.wav"},
-    'speaker_id': 1034,
-    'example_id': "1034_121119_000028_000001",
-}
 
-labels = load_speaker_labels(example, speaker_conditioning)
-label_options = ['Weight', 'Resonance', 'Breathiness', 'Roughness', 'Loudness', 'Strain', 'Pitch']
+def update_manipulation(manipulation_idx, example_id, transcription, manipulation_fkt):
+    global cached_example_id, cached_loaded_example, cached_labels, cached_d_vector, example_database, cached_unmanipulated
 
-# print('Estimated PVQ strengths of input speaker:')
-# max_len = max(len(name) for name in label_options)  
-# for label_name, pvq in zip(label_options, labels):
-    # print(f'{label_name:<{max_len}} : {pvq:6.2f}')
+    speaker_id = dataset_dict['dataset'][example_id]['speaker_id']
 
+    example = {
+        'audio_path': {'observation': f"./Dataset/Audio_files/{example_id}.wav"},
+        'd_vector_storage_root': f"./Saved_models/Dataset/Embeddings/{speaker_id}/{example_id}.pth",
+        'speaker_id': speaker_id,
+        'example_id': example_id,
+        'transcription': transcription
+    }
 
-def update_manipulation(manipulation_idx, manipulation_fkt):
+    if cached_example_id != example_id:
+        cached_loaded_example = load_audio_files(example)
+        cached_d_vector = torch.load(f"./Dataset/Embeddings/{speaker_id}/{example_id}.pth")
+        cached_labels = load_speaker_labels(example)
+        cached_example_id = example_id
+        cached_unmanipulated = tts_model.synthesize_from_example({
+            'text': transcription,
+            'd_vector': cached_d_vector.detach().numpy(),
+        })
 
-    d_vector = extract_speaker_embedding(example)
-    labels = load_speaker_labels(example, speaker_conditioning)
-    
     wav_manipulated = get_manipulation(
-        # example=example, 
-        d_vector=d_vector, 
-        labels=labels[None, :], 
+        example=example,
+        d_vector=cached_d_vector,
+        labels=cached_labels[None, :],
         flow=normalizing_flow,
         tts_model=tts_model,
         manipulation_idx=manipulation_idx,
         manipulation_fkt=manipulation_fkt,
+        config_norm_flow=config_norm_flow,
     )
-    
-    wav_unmanipulated = tts_model.synthesize_from_example({
-        'text': "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", 
-        'd_vector': d_vector.detach().numpy(),
-    })
-    sr = 24_000
-    return (sr, wav_unmanipulated), (sr, wav_manipulated)
-    
-    # with audio_output:
-    #     clear_output(wait=True) 
-    #     print('Manipulated Speaker')
-    #     display(Audio(wav_manipulated, rate=24_000, normalize=True))
-    #     print('Unmanipulated Synthese')
-    #     display(Audio(wav_unmanipulated, rate=24_000, normalize=True))
-    #     print('Original Speaker')
-    #     display(Audio(example['audio_path']['observation'], rate=24_000, normalize=True))
-
-    # print(f"Manipulated {label_options[manipulation_idx]} with strength {manipulation_fkt}")
-
-
-dropdown_options = [(label, i) for i, label in enumerate(label_options)]
+    return (24_000, cached_unmanipulated), (24_000, wav_manipulated)
+
+
 demo = gr.Interface(
     title="Perceptual Voice Quality (PVQ) Manipulation",
     fn=update_manipulation,
     inputs=[
-        gr.Dropdown(label="PVQ Feature", choices=dropdown_options, value=2, type="index"),
-        gr.Slider(label="Manipulation Factor", minimum=-2.0, maximum=2.0, value=1.0, step=0.1),
+        gr.Dropdown(
+            label="PVQ Feature",
+            choices=[('Weight', 0), ('Resonance', 1), ('Breathiness', 2), ('Roughness', 3), ('Creak', 7)],
+            value=2, type="value"
+        ),
+        gr.Dropdown(
+            choices=dataset_dict['dataset'].keys(),
+            value='1422_149735_000006_000000', type="value"
+        ),
+        gr.Textbox(
+            value="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
+            placeholder='Type something'
+        ),
+        gr.Slider(label="Manipulation Factor", minimum=-1.0, maximum=2.0, value=1.0, step=0.1),
     ],
     outputs=[gr.Audio(label="original utterance"), gr.Audio(label="manipulated utterance")],
 )

models/norm_flow/config.json

@@ -1,12 +1,15 @@
 {
-  "factory": "pvq_manipulation.models.ffjord.FFJORD",
-  "normalize": true,
-  "ode_function": {
-    "condition_dim": 7,
-    "factory": "pvq_manipulation.models.ode_functions.CNFNN",
-    "hidden_channels": [
-      512
-    ],
-    "input_dim": 256
-  }
-}
+  "model":{
+    "factory": "pvq_manipulation.models.ffjord.FFJORD",
+    "normalize": true,
+    "ode_function": {
+      "condition_dim": 8,
+      "factory": "pvq_manipulation.models.ode_functions.CNFNN",
+      "hidden_channels": [
+        512
+      ],
+      "input_dim": 256
+    }
+  },
+  "flag_remove_mean": true
+}