Update app.py

app.py

@@ -1,174 +1,174 @@
-from pathlib import Path
-import argparse
-import soundfile as sf
-import torch
-import io
-import argparse
-from matcha.hifigan.config import v1
-from matcha.hifigan.denoiser import Denoiser
-from matcha.hifigan.env import AttrDict
-from matcha.hifigan.models import Generator as HiFiGAN
-from matcha.models.matcha_tts import MatchaTTS
-from matcha.text import sequence_to_text, text_to_sequence
-from matcha.utils.utils import intersperse
-import gradio as gr
-import requests
-
-def download_file(url, save_path):
-    response = requests.get(url)
-    with open(save_path, 'wb') as file:
-        file.write(response.content)
-
-url_checkpoint = 'https://github.com/simonlobgromov/AkylAI_Matcha_Checkpoint/releases/download/Matcha-TTS/checkpoint_epoch.499.ckpt'
-save_checkpoint_path = './checkpoints/checkpoint.ckpt'
-url_generator = 'https://github.com/simonlobgromov/AkylAI_Matcha_HiFiGan/releases/download/Generator/generator_v1'
-save_generator_path = './checkpoints/generator'
-
-download_file(url_checkpoint, save_checkpoint_path)
-download_file(url_generator, save_generator_path)
-
-def load_matcha( checkpoint_path, device):
-    model = MatchaTTS.load_from_checkpoint(checkpoint_path, map_location=device)
-    _ = model.eval()
-    return model
-
-def load_hifigan(checkpoint_path, device):
-    h = AttrDict(v1)
-    hifigan = HiFiGAN(h).to(device)
-    hifigan.load_state_dict(torch.load(checkpoint_path, map_location=device)["generator"])
-    _ = hifigan.eval()
-    hifigan.remove_weight_norm()
-    return hifigan
-
-def load_vocoder(checkpoint_path, device):
-    vocoder = None
-    vocoder = load_hifigan(checkpoint_path, device)
-    denoiser = Denoiser(vocoder, mode="zeros")
-    return vocoder, denoiser
-
-def process_text(i: int, text: str, device: torch.device):
-    print(f"[{i}] - Input text: {text}")
-    x = torch.tensor(
-        intersperse(text_to_sequence(text, ["kyrgyz_cleaners"]), 0),
-        dtype=torch.long,
-        device=device,
-    )[None]
-    x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device)
-    x_phones = sequence_to_text(x.squeeze(0).tolist())
-    print(f"[{i}] - Phonetised text: {x_phones[1::2]}")
-    return {"x_orig": text, "x": x, "x_lengths": x_lengths, "x_phones": x_phones}
-
-def to_waveform(mel, vocoder, denoiser=None):
-    audio = vocoder(mel).clamp(-1, 1)
-    if denoiser is not None:
-        audio = denoiser(audio.squeeze(), strength=0.00025).cpu().squeeze()
-    return audio.cpu().squeeze()
-
-@torch.inference_mode()
-def process_text_gradio(text):
-    output = process_text(1, text, device)
-    return output["x_phones"][1::2], output["x"], output["x_lengths"]
-
-@torch.inference_mode()
-def synthesise_mel(text, text_length, n_timesteps, temperature, length_scale, spk=-1):
-    spk = torch.tensor([spk], device=device, dtype=torch.long) if spk >= 0 else None
-    output = model.synthesise(
-        text,
-        text_length,
-        n_timesteps=n_timesteps,
-        temperature=temperature,
-        spks=spk,
-        length_scale=length_scale,
-    )
-    output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
-    return output["waveform"].numpy()
-
-def get_inference(text, n_timesteps=20, mel_temp = 0.667, length_scale=0.8, spk=-1):
-    phones, text, text_lengths = process_text_gradio(text)
-    print(type(synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk)))
-    return synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk)
-
-
-device = torch.device("cpu")
-model_path = './checkpoints/checkpoint.ckpt'
-vocoder_path = './checkpoints/generator'
-model = load_matcha(model_path, device) 
-vocoder, denoiser = load_vocoder(vocoder_path, device) 
-
-def gen_tts(text, speaking_rate):
-    return 22050, get_inference(text = text, length_scale = speaking_rate)
-
-default_text = "Баарыңарга салам, менин атым Акылай."
-
-css = """
-        #share-btn-container {
-            display: flex;
-            padding-left: 0.5rem !important;
-            padding-right: 0.5rem !important;
-            background-color: #000000;
-            justify-content: center;
-            align-items: center;
-            border-radius: 9999px !important; 
-            width: 13rem;
-            margin-top: 10px;
-            margin-left: auto;
-            flex: unset !important;
-        }
-        #share-btn {
-            all: initial;
-            color: #ffffff;
-            font-weight: 600;
-            cursor: pointer;
-            font-family: 'IBM Plex Sans', sans-serif;
-            margin-left: 0.5rem !important;
-            padding-top: 0.25rem !important;
-            padding-bottom: 0.25rem !important;
-            right:0;
-        }
-        #share-btn * {
-            all: unset !important;
-        }
-        #share-btn-container div:nth-child(-n+2){
-            width: auto !important;
-            min-height: 0px !important;
-        }
-        #share-btn-container .wrap {
-            display: none !important;
-        }
-"""
-with gr.Blocks(css=css) as block:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
-              <div
-                style="
-                  display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;
-                "
-              >
-                <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
-                  Akyl-AI TTS
-                </h1>
-              </div>
-            </div>
-        """
-    )
-    with gr.Row():
-        image_path = "./photo_2024-04-07_15-59-52.png"
-        gr.Image(image_path, label=None, width=660, height=315, show_label=False)
-    with gr.Row():
-        with gr.Column():
-            input_text = gr.Textbox(label="Input Text", lines=2, value=default_text, elem_id="input_text")
-            speaking_rate = gr.Slider(label='Speaking rate', minimum=0.5, maximum=1, step=0.05, value=0.8, interactive=True, show_label=True, elem_id="speaking_rate")
-
-
-            run_button = gr.Button("Generate Audio", variant="primary")
-        with gr.Column():
-            audio_out = gr.Audio(label="Parler-TTS generation", type="numpy", elem_id="audio_out")
-
-    inputs = [input_text, speaking_rate]
-    outputs = [audio_out]
-    run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True)
-
-
-block.queue()
-block.launch(share=True)
+from pathlib import Path
+import argparse
+import soundfile as sf
+import torch
+import io
+import argparse
+from matcha.hifigan.config import v1
+from matcha.hifigan.denoiser import Denoiser
+from matcha.hifigan.env import AttrDict
+from matcha.hifigan.models import Generator as HiFiGAN
+from matcha.models.matcha_tts import MatchaTTS
+from matcha.text import sequence_to_text, text_to_sequence
+from matcha.utils.utils import intersperse
+import gradio as gr
+import requests
+
+def download_file(url, save_path):
+    response = requests.get(url)
+    with open(save_path, 'wb') as file:
+        file.write(response.content)
+
+url_checkpoint = 'https://github.com/simonlobgromov/AkylAI_Matcha_Checkpoint/releases/download/Matcha-TTS/checkpoint_epoch.499.ckpt'
+save_checkpoint_path = './checkpoints/checkpoint.ckpt'
+url_generator = 'https://github.com/simonlobgromov/AkylAI_Matcha_HiFiGan/releases/download/Generator/generator_v1'
+save_generator_path = './checkpoints/generator'
+
+download_file(url_checkpoint, save_checkpoint_path)
+download_file(url_generator, save_generator_path)
+
+def load_matcha( checkpoint_path, device):
+    model = MatchaTTS.load_from_checkpoint(checkpoint_path, map_location=device)
+    _ = model.eval()
+    return model
+
+def load_hifigan(checkpoint_path, device):
+    h = AttrDict(v1)
+    hifigan = HiFiGAN(h).to(device)
+    hifigan.load_state_dict(torch.load(checkpoint_path, map_location=device)["generator"])
+    _ = hifigan.eval()
+    hifigan.remove_weight_norm()
+    return hifigan
+
+def load_vocoder(checkpoint_path, device):
+    vocoder = None
+    vocoder = load_hifigan(checkpoint_path, device)
+    denoiser = Denoiser(vocoder, mode="zeros")
+    return vocoder, denoiser
+
+def process_text(i: int, text: str, device: torch.device):
+    print(f"[{i}] - Input text: {text}")
+    x = torch.tensor(
+        intersperse(text_to_sequence(text, ["kyrgyz_cleaners"]), 0),
+        dtype=torch.long,
+        device=device,
+    )[None]
+    x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device)
+    x_phones = sequence_to_text(x.squeeze(0).tolist())
+    print(f"[{i}] - Phonetised text: {x_phones[1::2]}")
+    return {"x_orig": text, "x": x, "x_lengths": x_lengths, "x_phones": x_phones}
+
+def to_waveform(mel, vocoder, denoiser=None):
+    audio = vocoder(mel).clamp(-1, 1)
+    if denoiser is not None:
+        audio = denoiser(audio.squeeze(), strength=0.00025).cpu().squeeze()
+    return audio.cpu().squeeze()
+
+@torch.inference_mode()
+def process_text_gradio(text):
+    output = process_text(1, text, device)
+    return output["x_phones"][1::2], output["x"], output["x_lengths"]
+
+@torch.inference_mode()
+def synthesise_mel(text, text_length, n_timesteps, temperature, length_scale, spk=-1):
+    spk = torch.tensor([spk], device=device, dtype=torch.long) if spk >= 0 else None
+    output = model.synthesise(
+        text,
+        text_length,
+        n_timesteps=n_timesteps,
+        temperature=temperature,
+        spks=spk,
+        length_scale=length_scale,
+    )
+    output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
+    return output["waveform"].numpy()
+
+def get_inference(text, n_timesteps=20, mel_temp = 0.667, length_scale=0.8, spk=-1):
+    phones, text, text_lengths = process_text_gradio(text)
+    print(type(synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk)))
+    return synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk)
+
+
+device = torch.device("cpu")
+model_path = './checkpoints/checkpoint.ckpt'
+vocoder_path = './checkpoints/generator'
+model = load_matcha(model_path, device) 
+vocoder, denoiser = load_vocoder(vocoder_path, device) 
+
+def gen_tts(text, speaking_rate):
+    return 22050, get_inference(text = text, length_scale = speaking_rate)
+
+default_text = "Баарыңарга салам, менин атым Акылай."
+
+css = """
+        #share-btn-container {
+            display: flex;
+            padding-left: 0.5rem !important;
+            padding-right: 0.5rem !important;
+            background-color: #000000;
+            justify-content: center;
+            align-items: center;
+            border-radius: 9999px !important; 
+            width: 13rem;
+            margin-top: 10px;
+            margin-left: auto;
+            flex: unset !important;
+        }
+        #share-btn {
+            all: initial;
+            color: #ffffff;
+            font-weight: 600;
+            cursor: pointer;
+            font-family: 'IBM Plex Sans', sans-serif;
+            margin-left: 0.5rem !important;
+            padding-top: 0.25rem !important;
+            padding-bottom: 0.25rem !important;
+            right:0;
+        }
+        #share-btn * {
+            all: unset !important;
+        }
+        #share-btn-container div:nth-child(-n+2){
+            width: auto !important;
+            min-height: 0px !important;
+        }
+        #share-btn-container .wrap {
+            display: none !important;
+        }
+"""
+with gr.Blocks(css=css) as block:
+    gr.HTML(
+        """
+            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
+              <div
+                style="
+                  display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;
+                "
+              >
+                <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
+                  Akyl-AI TTS
+                </h1>
+              </div>
+            </div>
+        """
+    )
+    with gr.Row():
+        image_path = "./photo_2024-04-07_15-59-52.png"
+        gr.Image(image_path, label=None, width=660, height=315, show_label=False)
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Input Text", lines=2, value=default_text, elem_id="input_text")
+            speaking_rate = gr.Slider(label='Speaking rate', minimum=0.5, maximum=1, step=0.05, value=0.8, interactive=True, show_label=True, elem_id="speaking_rate")
+
+
+            run_button = gr.Button("Generate Audio", variant="primary")
+        with gr.Column():
+            audio_out = gr.Audio(label="AkylAi-TTS", type="numpy", elem_id="audio_out")
+
+    inputs = [input_text, speaking_rate]
+    outputs = [audio_out]
+    run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True)
+
+
+block.queue()
+block.launch(share=True)