optimize(model): more torch funcalls (#451)

and fix wenui infer without refine text

ChatTTS/model/gpt.py

@@ -198,21 +198,15 @@ class GPT(nn.Module):
         if past_key_values is not None:
             if isinstance(past_key_values, Cache):
                 past_length = (
-                    cache_position[0]
+                    int(cache_position[0])
                     if cache_position is not None
                     else past_key_values.get_seq_length()
                 )
-                max_cache_length = (
-                    torch.tensor(
-                        past_key_values.get_max_length(), device=input_ids.device
-                    )
-                    if past_key_values.get_max_length() is not None
-                    else None
-                )
+                max_cache_length = past_key_values.get_max_length()
                 cache_length = (
                     past_length
                     if max_cache_length is None
-                    else torch.min(max_cache_length, past_length)
+                    else min(max_cache_length, past_length)
                 )
             # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
             else:
@@ -227,11 +221,12 @@ class GPT(nn.Module):
                 attention_mask is not None
                 and attention_mask.shape[1] > input_ids.shape[1]
             ):
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+                start = -(attention_mask.shape[1] - past_length)
+                input_ids = input_ids.narrow(1, start, -start)
             # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
             # input_ids based on the past_length.
             elif past_length < input_ids.shape[1]:
-                input_ids = input_ids[:, past_length:]
+                input_ids = input_ids.narrow(1, past_length, input_ids.size(1)-past_length)
             # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
 
             # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
@@ -240,14 +235,14 @@ class GPT(nn.Module):
                 and attention_mask is not None
                 and cache_length + input_ids.shape[1] > max_cache_length
             ):
-                attention_mask = attention_mask[:, -max_cache_length:]
+                attention_mask = attention_mask.narrow(1, -max_cache_length, max_cache_length)
 
         if attention_mask is not None and position_ids is None:
             # create position_ids on the fly for batch generation
             position_ids = attention_mask.long().cumsum(-1) - 1
             position_ids.masked_fill_(attention_mask == 0, 1)
             if past_key_values:
-                position_ids = position_ids[:, -input_ids.shape[1] :]
+                position_ids = position_ids.narrow(1, -input_ids.shape[1], input_ids.shape[1])
 
         input_length = (
             position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
@@ -257,7 +252,7 @@ class GPT(nn.Module):
                 past_length, past_length + input_length, device=input_ids.device
             )
         else:
-            cache_position = cache_position[-input_length:]
+            cache_position = cache_position.narrow(0, -input_length, input_length)
 
         if has_static_cache:
             past_key_values = None
@@ -365,7 +360,7 @@ class GPT(nn.Module):
                 device=inputs_ids.device,
             )
             if attention_mask is not None:
-                attention_mask_cache[:, : attention_mask.shape[1]] = attention_mask
+                attention_mask_cache.narrow(1, 0, attention_mask.shape[1]).copy_(attention_mask)
 
             with tqdm(
                 total=max_new_token,
@@ -379,7 +374,7 @@ class GPT(nn.Module):
                     model_input = self._prepare_generation_inputs(
                         inputs_ids,
                         past_key_values,
-                        attention_mask_cache[:, : inputs_ids.shape[1]],
+                        attention_mask_cache.narrow(1, 0, inputs_ids.shape[1]),
                         use_cache=True,
                     )
 

ChatTTS/model/processors.py

@@ -17,15 +17,19 @@ class CustomRepetitionPenaltyLogitsProcessorRepeat:
     def __call__(
         self, input_ids: torch.LongTensor, scores: torch.FloatTensor
     ) -> torch.FloatTensor:
-
-        input_ids = input_ids[:, -self.past_window :]
+        if input_ids.size(1) > self.past_window:
+            input_ids = input_ids.narrow(1, -self.past_window, self.past_window)
         freq = F.one_hot(input_ids, scores.size(1)).sum(1)
-        freq[self.max_input_ids :] = 0
-        alpha = self.penalty**freq
+        if freq.size(0) > self.max_input_ids:
+            freq.narrow(0, self.max_input_ids, freq.size(0)-self.max_input_ids).zero_()
+        alpha = torch.pow(self.penalty, freq)
         scores = scores.contiguous()
-        scores = torch.where(scores < 0, scores * alpha, scores / alpha)
-
-        return scores
+        inp = scores.multiply(alpha)
+        oth = scores.divide(alpha)
+        con = scores < 0
+        out = torch.where(con, inp, oth)
+        del inp, oth, scores, con, alpha
+        return out
 
 
 """class CustomRepetitionPenaltyLogitsProcessor():

examples/web/ex.py

@@ -0,0 +1,29 @@
+ex=[
+    [
+        "四川美食确实以辣闻名，但也有不辣的选择。比如甜水面、赖汤圆、蛋烘糕、叶儿粑等，这些小吃口味温和，甜而不腻，也很受欢迎。",
+        0.3,
+        0.7,
+        20,
+        2,
+        42,
+        True,
+    ],
+    [
+        "What is your favorite english food?",
+        0.5,
+        0.5,
+        10,
+        245,
+        531,
+        True,
+    ],
+    [
+        "chat T T S is a text to speech model designed for dialogue applications. [uv_break]it supports mixed language input [uv_break]and offers multi speaker capabilities with precise control over prosodic elements like [uv_break]laughter[uv_break][laugh], [uv_break]pauses, [uv_break]and intonation. [uv_break]it delivers natural and expressive speech,[uv_break]so please[uv_break] use the project responsibly at your own risk.[uv_break]",
+        0.8,
+        0.4,
+        7,
+        70,
+        165,
+        False,
+    ],
+]

examples/web/funcs.py

@@ -1,6 +1,7 @@
 import sys
 import random
 from typing import Optional
+from time import sleep
 
 import gradio as gr
 import numpy as np
@@ -107,6 +108,7 @@ def refine_text(
     has_interrupted = False
 
     if not refine_text_flag:
+        sleep(1) # to skip fast answer of loading mark
         return text, *set_generate_buttons(
             generate_button, interrupt_button, is_reset=True
         )

examples/web/webui.py

@@ -11,6 +11,7 @@ import argparse
 import gradio as gr
 
 from examples.web.funcs import *
+from examples.web.ex import ex
 
 
 def main():
@@ -35,7 +36,7 @@ def main():
                 maximum=1.0,
                 step=0.00001,
                 value=0.3,
-                label="Audio temperature",
+                label="Audio Temperature",
                 interactive=True,
             )
             top_p_slider = gr.Slider(
@@ -54,7 +55,7 @@ def main():
             voice_selection = gr.Dropdown(
                 label="Timbre", choices=voices.keys(), value="Default"
             )
-            audio_seed_input = gr.Number(value=2, label="Audio Seed")
+            audio_seed_input = gr.Number(value=2, label="Audio Seed", interactive=True)
             generate_audio_seed = gr.Button("\U0001F3B2")
             text_seed_input = gr.Number(value=42, label="Text Seed")
             generate_text_seed = gr.Button("\U0001F3B2")
@@ -144,35 +145,7 @@ def main():
             )
 
         gr.Examples(
-            examples=[
-                [
-                    "四川美食确实以辣闻名，但也有不辣的选择。比如甜水面、赖汤圆、蛋烘糕、叶儿粑等，这些小吃口味温和，甜而不腻，也很受欢迎。",
-                    0.3,
-                    0.7,
-                    20,
-                    2,
-                    42,
-                    True,
-                ],
-                [
-                    "What is [uv_break]your favorite english food?[laugh][lbreak]",
-                    0.5,
-                    0.5,
-                    10,
-                    245,
-                    531,
-                    False,
-                ],
-                [
-                    "chat T T S is a text to speech model designed for dialogue applications. [uv_break]it supports mixed language input [uv_break]and offers multi speaker capabilities with precise control over prosodic elements [laugh]like like [uv_break]laughter[laugh], [uv_break]pauses, [uv_break]and intonation. [uv_break]it delivers natural and expressive speech,[uv_break]so please[uv_break] use the project responsibly at your own risk.[uv_break]",
-                    0.2,
-                    0.6,
-                    15,
-                    67,
-                    165,
-                    False,
-                ],
-            ],
+            examples=ex,
             inputs=[
                 text_input,
                 temperature_slider,
@@ -213,6 +186,7 @@ def main():
         server_port=args.server_port,
         root_path=args.root_path,
         inbrowser=True,
+        show_api=False,
     )