Cloyir/index-tts2-ForDgxSpark
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将 sample_kwargs 替换为 generation_kwargs

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yrom 2025-05-18 16:43:44 +08:00
parent 96d3b75708
commit 60a2238eac
1 changed files with 22 additions and 20 deletions
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42
indextts/infer.py
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@ -273,7 +273,7 @@ class IndexTTS:
self.gr_progress(value, desc=desc)
# 快速推理：对于“多句长文本”，可实现至少 2~10 倍以上的速度提升~ （First modified by sunnyboxs 2025-04-16）
def infer_fast(self, audio_prompt, text, output_path, verbose=False, max_text_tokens_per_sentence=100, sentences_bucket_max_size=4, **sample_kwargs):
def infer_fast(self, audio_prompt, text, output_path, verbose=False, max_text_tokens_per_sentence=100, sentences_bucket_max_size=4, **generation_kwargs):
"""
Args:
``max_text_tokens_per_sentence``: 分句的最大token数，默认``100``，可以根据GPU硬件情况调整
@ -321,15 +321,15 @@ class IndexTTS:
print(" splited sentences count:", len(sentences))
print(" max_text_tokens_per_sentence:", max_text_tokens_per_sentence)
print(*sentences, sep="\n")
do_sample = sample_kwargs.pop("do_sample", True)
top_p = sample_kwargs.pop("top_p", 0.8)
top_k = sample_kwargs.pop("top_k", 30)
temperature = sample_kwargs.pop("temperature", 1.0)
do_sample = generation_kwargs.pop("do_sample", True)
top_p = generation_kwargs.pop("top_p", 0.8)
top_k = generation_kwargs.pop("top_k", 30)
temperature = generation_kwargs.pop("temperature", 1.0)
autoregressive_batch_size = 1
length_penalty = sample_kwargs.pop("length_penalty", 0.0)
num_beams = sample_kwargs.pop("num_beams", 3)
repetition_penalty = sample_kwargs.pop("repetition_penalty", 10.0)
max_mel_tokens = sample_kwargs.pop("max_mel_tokens", 600)
length_penalty = generation_kwargs.pop("length_penalty", 0.0)
num_beams = generation_kwargs.pop("num_beams", 3)
repetition_penalty = generation_kwargs.pop("repetition_penalty", 10.0)
max_mel_tokens = generation_kwargs.pop("max_mel_tokens", 600)
sampling_rate = 24000
# lang = "EN"
# lang = "ZH"
@ -391,7 +391,8 @@ class IndexTTS:
length_penalty=length_penalty,
num_beams=num_beams,
repetition_penalty=repetition_penalty,
max_generate_length=max_mel_tokens)
max_generate_length=max_mel_tokens,
**generation_kwargs)
all_batch_codes.append(temp_codes)
gpt_gen_time += time.perf_counter() - m_start_time
@ -494,7 +495,7 @@ class IndexTTS:
return (sampling_rate, wav_data)
# 原始推理模式
def infer(self, audio_prompt, text, output_path, verbose=False, max_text_tokens_per_sentence=120, **sample_kwargs):
def infer(self, audio_prompt, text, output_path, verbose=False, max_text_tokens_per_sentence=120, **generation_kwargs):
print(">> start inference...")
self._set_gr_progress(0, "start inference...")
if verbose:
@ -529,15 +530,15 @@ class IndexTTS:
print("sentences count:", len(sentences))
print("max_text_tokens_per_sentence:", max_text_tokens_per_sentence)
print(*sentences, sep="\n")
do_sample = sample_kwargs.pop("do_sample", True)
top_p = sample_kwargs.pop("top_p", 0.8)
top_k = sample_kwargs.pop("top_k", 30)
temperature = sample_kwargs.pop("temperature", 1.0)
do_sample = generation_kwargs.pop("do_sample", True)
top_p = generation_kwargs.pop("top_p", 0.8)
top_k = generation_kwargs.pop("top_k", 30)
temperature = generation_kwargs.pop("temperature", 1.0)
autoregressive_batch_size = 1
length_penalty = sample_kwargs.pop("length_penalty", 0.0)
num_beams = sample_kwargs.pop("num_beams", 3)
repetition_penalty = sample_kwargs.pop("repetition_penalty", 10.0)
max_mel_tokens = sample_kwargs.pop("max_mel_tokens", 600)
length_penalty = generation_kwargs.pop("length_penalty", 0.0)
num_beams = generation_kwargs.pop("num_beams", 3)
repetition_penalty = generation_kwargs.pop("repetition_penalty", 10.0)
max_mel_tokens = generation_kwargs.pop("max_mel_tokens", 600)
sampling_rate = 24000
# lang = "EN"
# lang = "ZH"
@ -579,7 +580,8 @@ class IndexTTS:
length_penalty=length_penalty,
num_beams=num_beams,
repetition_penalty=repetition_penalty,
max_generate_length=max_mel_tokens)
max_generate_length=max_mel_tokens,
**generation_kwargs)
gpt_gen_time += time.perf_counter() - m_start_time
if not has_warned and (codes[:, -1] != self.stop_mel_token).any():
warnings.warn(