#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Reproduce the turkish-stt WER/CER benchmark on a shared eval set.

    pip install sherpa-onnx soundfile librosa jiwer num2words huggingface_hub
    python run_eval.py --data-dir tts-synthetic            # weights auto-download from HF
    # or with local weights:
    python run_eval.py --data-dir tts-synthetic --model-dir /path/to/weights

Uses the published weights (encoder.int8.onnx + decoder.onnx + joiner.int8.onnx), applies one
matched Turkish normalization, computes WER/CER with jiwer, and writes raw hypotheses to
hypotheses.tsv for independent verification. Tested with sherpa-onnx 1.13.4.
"""
import argparse, csv, os, re
import numpy as np, soundfile as sf, sherpa_onnx, jiwer
from num2words import num2words

REPO = "mihuai/turkish-stt"

def files(model_dir):
    if model_dir:
        j = lambda f: os.path.join(model_dir, f)
        return j("tokens.txt"), j("encoder.int8.onnx"), j("decoder.onnx"), j("joiner.int8.onnx")
    from huggingface_hub import hf_hub_download
    d = lambda f: hf_hub_download(REPO, f)
    return d("tokens.txt"), d("encoder.int8.onnx"), d("decoder.onnx"), d("joiner.int8.onnx")

def norm(t):
    if not t: return ""
    for a,b in [("İ","i"),("I","ı"),("Ş","ş"),("Ğ","ğ"),("Ü","ü"),("Ö","ö"),("Ç","ç")]: t=t.replace(a,b)
    t=t.lower(); t=re.sub(r"(\d)\s+(\d)",r"\1\2",t)
    t=re.sub(r"\d+",lambda m:num2words(int(m.group()),lang="tr"),t)
    t=re.sub(r"[^a-zâîûçğıöşü\s]"," ",t); return re.sub(r"\s+"," ",t).strip()

def main():
    ap=argparse.ArgumentParser()
    ap.add_argument("--data-dir", default="tts-synthetic")
    ap.add_argument("--model-dir", default=None)
    a=ap.parse_args()
    tok,enc,dec,joi=files(a.model_dir)
    rec=sherpa_onnx.OnlineRecognizer.from_transducer(tokens=tok,encoder=enc,decoder=dec,joiner=joi,
        num_threads=2,decoding_method="modified_beam_search",provider="cpu")
    def stt(p):
        x,sr=sf.read(p,dtype="float32")
        if x.ndim>1: x=x.mean(axis=1)
        if sr!=16000:
            import librosa; x=librosa.resample(x,orig_sr=sr,target_sr=16000)
        s=rec.create_stream(); s.accept_waveform(16000,x); s.accept_waveform(16000,np.zeros(8000,dtype=np.float32)); s.input_finished()
        while rec.is_ready(s): rec.decode_stream(s)
        r=rec.get_result(s); return r if isinstance(r,str) else r.text
    H,R,rows=[],[],[]
    with open(os.path.join(a.data_dir,"manifest.tsv"),encoding="utf-8") as f:
        for row in csv.DictReader(f,delimiter="\t"):
            hyp=stt(os.path.join(a.data_dir,row["audio"]))
            H.append(norm(hyp)); R.append(norm(row["reference"]))
            rows.append((row["audio"],row["reference"],hyp))
    with open("hypotheses.tsv","w",encoding="utf-8") as f:
        f.write("audio\treference\thypothesis\n")
        for r in rows: f.write("\t".join(r)+"\n")
    print("clips: %d | WER %.2f%% | CER %.2f%% (raw hypotheses -> hypotheses.tsv)"%(
        len(R),jiwer.wer(R,H)*100,jiwer.cer(R,H)*100))

if __name__=="__main__": main()