F5 tts (#59)

* init tts/F5-TTS

* epic hax to download ckpts

* clear default reference audio/text from inference vars

* allow blank inputs for inference-cli to just pull checkpoints without running inference

* f5-tts github workflow to publish docker images

tts/F5-TTS/scripts/eval_librispeech_test_clean.py

@@ -0,0 +1,67 @@
+# Evaluate with Librispeech test-clean, ~3s prompt to generate 4-10s audio (the way of valle/voicebox evaluation)
+
+import sys, os
+sys.path.append(os.getcwd())
+
+import multiprocessing as mp
+import numpy as np
+
+from model.utils import (
+    get_librispeech_test,
+    run_asr_wer,
+    run_sim,
+)
+
+
+eval_task = "wer"  # sim | wer
+lang = "en"
+metalst = "data/librispeech_pc_test_clean_cross_sentence.lst"
+librispeech_test_clean_path = "<SOME_PATH>/LibriSpeech/test-clean"  # test-clean path
+gen_wav_dir = "PATH_TO_GENERATED"  # generated wavs
+
+gpus = [0,1,2,3,4,5,6,7]
+test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path)
+
+## In LibriSpeech, some speakers utilized varying voice characteristics for different characters in the book,
+## leading to a low similarity for the ground truth in some cases.
+# test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth = True)  # eval ground truth
+
+local = False
+if local:  # use local custom checkpoint dir
+    asr_ckpt_dir = "../checkpoints/Systran/faster-whisper-large-v3"
+else:
+    asr_ckpt_dir = ""  # auto download to cache dir
+
+wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
+
+
+# --------------------------- WER ---------------------------
+
+if eval_task == "wer":
+    wers = []
+
+    with mp.Pool(processes=len(gpus)) as pool:
+        args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set]
+        results = pool.map(run_asr_wer, args)
+        for wers_ in results:
+            wers.extend(wers_)
+
+    wer = round(np.mean(wers)*100, 3)
+    print(f"\nTotal {len(wers)} samples")
+    print(f"WER      : {wer}%")
+
+
+# --------------------------- SIM ---------------------------
+
+if eval_task == "sim":
+    sim_list = []
+
+    with mp.Pool(processes=len(gpus)) as pool:
+        args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set]
+        results = pool.map(run_sim, args)
+        for sim_ in results:
+            sim_list.extend(sim_)
+
+    sim = round(sum(sim_list)/len(sim_list), 3)
+    print(f"\nTotal {len(sim_list)} samples")
+    print(f"SIM      : {sim}")