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https://github.com/SWivid/F5-TTS.git
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add and run pre-commit with ruff
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@@ -1,6 +1,8 @@
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# Evaluate with Librispeech test-clean, ~3s prompt to generate 4-10s audio (the way of valle/voicebox evaluation)
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import sys, os
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import sys
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import os
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sys.path.append(os.getcwd())
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import multiprocessing as mp
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@@ -19,7 +21,7 @@ metalst = "data/librispeech_pc_test_clean_cross_sentence.lst"
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librispeech_test_clean_path = "<SOME_PATH>/LibriSpeech/test-clean" # test-clean path
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gen_wav_dir = "PATH_TO_GENERATED" # generated wavs
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gpus = [0,1,2,3,4,5,6,7]
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gpus = [0, 1, 2, 3, 4, 5, 6, 7]
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test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path)
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## In LibriSpeech, some speakers utilized varying voice characteristics for different characters in the book,
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@@ -46,7 +48,7 @@ if eval_task == "wer":
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for wers_ in results:
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wers.extend(wers_)
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wer = round(np.mean(wers)*100, 3)
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wer = round(np.mean(wers) * 100, 3)
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print(f"\nTotal {len(wers)} samples")
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print(f"WER : {wer}%")
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@@ -62,6 +64,6 @@ if eval_task == "sim":
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for sim_ in results:
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sim_list.extend(sim_)
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sim = round(sum(sim_list)/len(sim_list), 3)
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sim = round(sum(sim_list) / len(sim_list), 3)
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print(f"\nTotal {len(sim_list)} samples")
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print(f"SIM : {sim}")
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